def spruce_up_var_m( refdata ): from utilities import print_msg from utilities import model_circle, get_im from filter import filt_tanl, filt_gaussl from morphology import threshold import os numref = refdata[0] outdir = refdata[1] fscc = refdata[2] total_iter = refdata[3] varf = refdata[4] mask = refdata[5] ali50S = refdata[6] if ali50S: mask_50S = get_im( "mask-50S.spi" ) if fscc is None: flmin = 0.4 aamin = 0.1 else: flmin,aamin,idmin=minfilt( fscc ) aamin = aamin msg = "Minimum tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fflmin, aamin) print_msg(msg) for i in xrange(numref): volf = get_im( os.path.join(outdir, "vol%04d.hdf"% total_iter) , i ) if(not (varf is None) ): volf = volf.filter_by_image( varf ) volf = filt_tanl(volf, flmin, aamin) stat = Util.infomask(volf, mask, True) volf -= stat[0] Util.mul_scalar(volf, 1.0/stat[1]) nx = volf.get_xsize() stat = Util.infomask(volf,model_circle(nx//2-2,nx,nx,nx)-model_circle(nx//2-6,nx,nx,nx), True) volf -= stat[0] Util.mul_img( volf, mask ) volf = threshold(volf) volf = filt_gaussl( volf, 0.4) if ali50S: if i==0: v50S_0 = volf.copy() v50S_0 *= mask_50S else: from applications import ali_vol_3 from fundamentals import rot_shift3D v50S_i = volf.copy() v50S_i *= mask_50S params = ali_vol_3(v50S_i, v50S_0, 10.0, 0.5, mask=mask_50S) volf = rot_shift3D( volf, params[0], params[1], params[2], params[3], params[4], params[5], 1.0) volf.write_image( os.path.join(outdir, "volf%04d.hdf"%total_iter), i )
def test_ccf_circle_2D_31x20(self): """test ccf*: circle 2D, 31x20..........................""" from utilities import model_circle from fundamentals import ccf, ccfn, ccfnp, ccfnpl, ccfp, ccfpl A = model_circle(7, 31, 20) B = model_circle(4, 31, 20) self.internal_check_ccf(A, B, ccf(A,B,False), ccfn(A,B,False), ccfp(A,B,False), ccfnp(A,B,False), ccfpl(A,B,False), ccfnpl(A,B,False) , ccf(A,B,True ), ccfn(A,B,True ), ccfp(A,B,True ), ccfnp(A,B,True ), ccfpl(A,B,True ), ccfnpl(A,B,True ) )
def spruce_up_variance(ref_data):
from utilities import print_msg
from filter import filt_tanl, fit_tanh, filt_gaussl
from morphology import threshold
# Prepare the reference in 3D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# 4 1.0/variance
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
mask = ref_data[0]
center = ref_data[1]
vol = ref_data[2]
fscc = ref_data[3]
varf = ref_data[4]
print_msg("spruce_up with variance\n")
cs = [0.0] * 3
if not (varf is None):
volf = vol.filter_by_image(varf)
#fl, aa = fit_tanh(ref_data[3])
fl = 0.22
aa = 0.15
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % (
fl, aa)
print_msg(msg)
volf = filt_tanl(volf, fl, aa)
stat = Util.infomask(volf, None, True)
volf = volf - stat[0]
Util.mul_scalar(volf, 1.0 / stat[1])
from utilities import model_circle
nx = volf.get_xsize()
stat = Util.infomask(
volf,
model_circle(nx // 2 - 2, nx, nx, nx) -
model_circle(nx // 2 - 6, nx, nx, nx), True)
volf -= stat[0]
Util.mul_img(volf, mask)
volf = threshold(volf)
volf = filt_gaussl(volf, 0.4)
return volf, cs
def test_acf_circle_2D_31x20(self): """test acf*: circle 2D, 31x20..........................""" from utilities import model_circle from fundamentals import acf, acfn, acfnp, acfnpl, acfp, acfpl A = model_circle(7, 31, 20) self.internal_check_ccf(A, A, acf(A,False), acfn(A,False), acfp(A,False), acfnp(A,False), acfpl(A, False), acfnpl(A, False) , acf(A,True ), acfn(A,True ), acfp(A,True ), acfnp(A,True ), acfpl(A, True ), acfnpl(A, True ) )
def constant(ref_data):
from utilities import print_msg
from filter import fit_tanh, filt_tanl
from utilities import center_2D
from morphology import threshold
# Prepare the reference in 2D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FRC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
#print_msg("steady #%6d\n"%(ref_ali2d_counter))
fl = 0.4
aa = 0.1
#msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
#print_msg(msg)
from utilities import model_circle
nx = ref_data[2].get_xsize()
stat = Util.infomask(ref_data[2], model_circle(nx // 2 - 2, nx, nx), False)
ref_data[2] -= stat[0]
#tavg = filt_tanl(threshold(ref_data[2]), fl, aa)
tavg = filt_tanl(ref_data[2], fl, aa)
cs = [0.0] * 2
return tavg, cs
def volshiftali(vv, mask3d=None): nv = len(vv) ni = vv[0].get_xsize() if(mask3d == None): mask3d = model_circle(ni//2-2, ni, ni, ni) for i in xrange(nv): Util.mul_img(vv[i], mask3d) fftip(vv[i]) del mask3d ps = [[0.,0.,0.] for i in xrange(nv)] changed = True while(changed): a = EMData(ni, ni, ni, False) for i in vv: Util.add_img(a,i) changed = False for i in xrange(nv): pp = peak_search(ccf(vv[i],a), 1) for j in xrange(3): if(pp[0][5+j] != ps[i][j]): ps[i][j] = pp[0][5+j] changed = True return ps
def constant( ref_data ):
from utilities import print_msg
from filter import fit_tanh, filt_tanl
from utilities import center_2D
from morphology import threshold
# Prepare the reference in 2D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FRC) to reference image:
global ref_ali2d_counter
ref_ali2d_counter += 1
#print_msg("steady #%6d\n"%(ref_ali2d_counter))
fl = 0.4
aa = 0.1
#msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
#print_msg(msg)
from utilities import model_circle
nx = ref_data[2].get_xsize()
stat = Util.infomask(ref_data[2], model_circle(nx//2-2,nx,nx), False)
ref_data[2] -= stat[0]
#tavg = filt_tanl(threshold(ref_data[2]), fl, aa)
tavg = filt_tanl(ref_data[2], fl, aa)
cs = [0.0]*2
return tavg, cs
def spruce_up_variance( ref_data ):
from utilities import print_msg
from filter import filt_tanl, fit_tanh, filt_gaussl
from morphology import threshold
# Prepare the reference in 3D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# 4 1.0/variance
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
mask = ref_data[0]
center = ref_data[1]
vol = ref_data[2]
fscc = ref_data[3]
varf = ref_data[4]
print_msg("spruce_up with variance\n")
cs = [0.0]*3
if not(varf is None):
volf = vol.filter_by_image(varf)
#fl, aa = fit_tanh(ref_data[3])
fl = 0.22
aa = 0.15
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
print_msg(msg)
volf = filt_tanl(volf, fl, aa)
stat = Util.infomask(volf, None, True)
volf = volf - stat[0]
Util.mul_scalar(volf, 1.0/stat[1])
from utilities import model_circle
nx = volf.get_xsize()
stat = Util.infomask(volf, model_circle(nx//2-2,nx,nx,nx)-model_circle(nx//2-6,nx,nx,nx), True)
volf -= stat[0]
Util.mul_img(volf, mask)
volf = threshold(volf)
volf = filt_gaussl(volf, 0.4)
return volf, cs
def spruce_up(ref_data):
from utilities import print_msg
from filter import filt_tanl, fit_tanh
from morphology import threshold
# Prepare the reference in 3D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
print_msg("Changed4 spruce_up\n")
cs = [0.0] * 3
stat = Util.infomask(ref_data[2], None, True)
volf = ref_data[2] - stat[0]
Util.mul_scalar(volf, 1.0 / stat[1])
volf = threshold(volf)
# Apply B-factor
from filter import filt_gaussinv
from math import sqrt
B = 1.0 / sqrt(2. * 14.0)
volf = filt_gaussinv(volf, B, False)
nx = volf.get_xsize()
from utilities import model_circle
stat = Util.infomask(
volf,
model_circle(nx // 2 - 2, nx, nx, nx) -
model_circle(nx // 2 - 6, nx, nx, nx), True)
volf -= stat[0]
Util.mul_img(volf, ref_data[0])
fl, aa = fit_tanh(ref_data[3])
#fl = 0.35
#aa = 0.1
aa /= 2
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % (
fl, aa)
print_msg(msg)
volf = filt_tanl(volf, fl, aa)
return volf, cs
def adaptive_mask2D(img, nsigma = 1.0, ndilation = 3, kernel_size = 11, gauss_standard_dev =9): """ Name adaptive_mask - create a mask from a given image. Input img: input image nsigma: value for initial thresholding of the image. Output mask: The mask will have values one, zero, with Gaussian smooth transition between two regions. """ from utilities import gauss_edge, model_circle from morphology import binarize, dilation nx = img.get_xsize() ny = img.get_ysize() mc = model_circle(nx//2, nx, ny) - model_circle(nx//3, nx, ny) s1 = Util.infomask(img, mc, True) mask = Util.get_biggest_cluster(binarize(img, s1[0]+s1[1]*nsigma)) for i in xrange(ndilation): mask = dilation(mask) #mask = gauss_edge(mask, kernel_size, gauss_standard_dev) return mask
def spruce_up( ref_data ):
from utilities import print_msg
from filter import filt_tanl, fit_tanh
from morphology import threshold
# Prepare the reference in 3D alignment, i.e., low-pass filter and center.
# Input: list ref_data
# 0 - mask
# 1 - center flag
# 2 - raw average
# 3 - fsc result
# Output: filtered, centered, and masked reference image
# apply filtration (FSC) to reference image:
print_msg("Changed4 spruce_up\n")
cs = [0.0]*3
stat = Util.infomask(ref_data[2], None, True)
volf = ref_data[2] - stat[0]
Util.mul_scalar(volf, 1.0/stat[1])
volf = threshold(volf)
# Apply B-factor
from filter import filt_gaussinv
from math import sqrt
B = 1.0/sqrt(2.*14.0)
volf = filt_gaussinv(volf, B, False)
nx = volf.get_xsize()
from utilities import model_circle
stat = Util.infomask(volf, model_circle(nx//2-2,nx,nx,nx)-model_circle(nx//2-6,nx,nx,nx), True)
volf -= stat[0]
Util.mul_img(volf, ref_data[0])
fl, aa = fit_tanh(ref_data[3])
#fl = 0.35
#aa = 0.1
aa /= 2
msg = "Tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n"%(fl, aa)
print_msg(msg)
volf = filt_tanl(volf, fl, aa)
return volf, cs
def get_resolution(vol, radi, nnxo, fscoutputdir):
# this function is single processor
# Get updated FSC curves
if(ali3d_options.mask3D is None): mask = model_circle(radi,nnxo,nnxo,nnxo)
else: mask = get_im(ali3d_options.mask3D)
nfsc = fsc(vol[0]*mask,vol[1]*mask, 1.0,os.path.join(fscoutputdir,"fsc.txt") )
currentres = 0.5
ns = len(nfsc[1])
for i in xrange(1,ns-1):
if ( (2*nfsc[1][i]/(1.0+nfsc[1][i]) ) < 0.5):
currentres = nfsc[0][i-1]
break
#print(" Current resolution ",i,currentres)
if(currentres < 0.0):
print(" Something wrong with the resolution, cannot continue")
mpi_finalize()
exit()
return currentres
def model_circle(rad, x, y):
''' Create a model circle
* has alternative
:Parameters:
rad : int
Radius
x : int
Width of the image
y : int
Height of the image
:Returns:
model : array
Image with disk of radius `rad`
'''
if utilities is None: raise ImportError, "EMAN2/Sparx library not available, model_circle requires EMAN2/Sparx"
emdata = utilities.model_circle(rad, x, y)
return em2numpy(emdata).copy()
def model_circle(rad, x, y):
''' Create a model circle
* has alternative
:Parameters:
rad : int
Radius
x : int
Width of the image
y : int
Height of the image
:Returns:
model : array
Image with disk of radius `rad`
'''
if utilities is None:
raise ImportError, "EMAN2/Sparx library not available, model_circle requires EMAN2/Sparx"
emdata = utilities.model_circle(rad, x, y)
return em2numpy(emdata).copy()
def shiftali_MPI(stack,
maskfile=None,
maxit=100,
CTF=False,
snr=1.0,
Fourvar=False,
search_rng=-1,
oneDx=False,
search_rng_y=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from pap_statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
from utilities import get_params2D, set_params2D
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from EMAN2 import Processor
from time import time
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("shiftali_MPI")
max_iter = int(maxit)
if myid == main_node:
if ftp == "bdb":
from EMAN2db import db_open_dict
dummy = db_open_dict(stack, True)
nima = EMUtil.get_image_count(stack)
else:
nima = 0
nima = bcast_number_to_all(nima, source_node=main_node)
list_of_particles = list(range(nima))
image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start:image_end]
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
ima = EMData()
ima.read_image(stack, list_of_particles[0], True)
nx = ima.get_xsize()
ny = ima.get_ysize()
if CTF: ctf_app = ima.get_attr_default('ctf_applied', 2)
del ima
else:
nx = 0
ny = 0
if CTF: ctf_app = 0
nx = bcast_number_to_all(nx, source_node=main_node)
ny = bcast_number_to_all(ny, source_node=main_node)
if CTF:
ctf_app = bcast_number_to_all(ctf_app, source_node=main_node)
if ctf_app > 0:
ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)
if maskfile == None:
mrad = min(nx, ny)
mask = model_circle(mrad // 2 - 2, nx, ny)
else:
mask = get_im(maskfile)
if CTF:
from filter import filt_ctf
from morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
from global_def import CACHE_DISABLE
if CACHE_DISABLE:
data = EMData.read_images(stack, list_of_particles)
else:
for i in range(number_of_proc):
if myid == i:
data = EMData.read_images(stack, list_of_particles)
if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)
for im in range(len(data)):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
else:
ctf_2_sum = None
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
for i in range(0, nx, 2):
for j in range(ny):
temp.set_value_at(i, j, snr)
Util.add_img(ctf_2_sum, temp)
del temp
total_iter = 0
# apply initial xform.align2d parameters stored in header
init_params = []
for im in range(len(data)):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im],
p['alpha'],
sx=p['tx'],
sy=p['ty'],
mirror=p['mirror'],
scale=p['scale'])
# fourier transform all images, and apply ctf if CTF
for im in range(len(data)):
if CTF:
ctf_params = data[im].get_attr("ctf")
data[im] = filt_ctf(fft(data[im]), ctf_params)
else:
data[im] = fft(data[im])
sx_sum = 0
sy_sum = 0
sx_sum_total = 0
sy_sum_total = 0
shift_x = [0.0] * len(data)
shift_y = [0.0] * len(data)
ishift_x = [0.0] * len(data)
ishift_y = [0.0] * len(data)
for Iter in range(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n" % (total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in data:
Util.add_img(avg, im)
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF:
tavg = Util.divn_filter(avg, ctf_2_sum)
else:
tavg = Util.mult_scalar(avg, 1.0 / float(nima))
else:
tavg = EMData(nx, ny, 1, False)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask(tavg, mask, False)
tavg -= stat[0]
Util.mul_img(tavg, mask)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
tavg = fft(tavg)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
sx_sum = 0
sy_sum = 0
if search_rng > 0: nwx = 2 * search_rng + 1
else: nwx = nx
if search_rng_y > 0: nwy = 2 * search_rng_y + 1
else: nwy = ny
not_zero = 0
for im in range(len(data)):
if oneDx:
ctx = Util.window(ccf(data[im], tavg), nwx, 1)
p1 = peak_search(ctx)
p1_x = -int(p1[0][3])
ishift_x[im] = p1_x
sx_sum += p1_x
else:
p1 = peak_search(Util.window(ccf(data[im], tavg), nwx, nwy))
p1_x = -int(p1[0][4])
p1_y = -int(p1[0][5])
ishift_x[im] = p1_x
ishift_y[im] = p1_y
sx_sum += p1_x
sy_sum += p1_y
if not_zero == 0:
if (not (ishift_x[im] == 0.0)) or (not (ishift_y[im] == 0.0)):
not_zero = 1
sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if not oneDx:
sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
sx_sum_total = int(sx_sum[0])
if not oneDx:
sy_sum_total = int(sy_sum[0])
else:
sx_sum_total = 0
sy_sum_total = 0
sx_sum_total = bcast_number_to_all(sx_sum_total, source_node=main_node)
if not oneDx:
sy_sum_total = bcast_number_to_all(sy_sum_total,
source_node=main_node)
sx_ave = round(float(sx_sum_total) / nima)
sy_ave = round(float(sy_sum_total) / nima)
for im in range(len(data)):
p1_x = ishift_x[im] - sx_ave
p1_y = ishift_y[im] - sy_ave
params2 = {
"filter_type": Processor.fourier_filter_types.SHIFT,
"x_shift": p1_x,
"y_shift": p1_y,
"z_shift": 0.0
}
data[im] = Processor.EMFourierFilter(data[im], params2)
shift_x[im] += p1_x
shift_y[im] += p1_y
# stop if all shifts are zero
not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node,
MPI_COMM_WORLD)
if myid == main_node:
not_zero_all = int(not_zero[0])
else:
not_zero_all = 0
not_zero_all = bcast_number_to_all(not_zero_all, source_node=main_node)
if myid == main_node:
print_msg("Time of iteration = %12.2f\n" % (time() - start_time))
start_time = time()
if not_zero_all == 0: break
#for im in xrange(len(data)): data[im] = fft(data[im]) This should not be required as only header information is used
# combine shifts found with the original parameters
for im in range(len(data)):
t0 = init_params[im]
t1 = Transform()
t1.set_params({
"type": "2D",
"alpha": 0,
"scale": t0.get_scale(),
"mirror": 0,
"tx": shift_x[im],
"ty": shift_y[im]
})
# combine t0 and t1
tt = t1 * t0
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if (file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node: print_end_msg("shiftali_MPI")
def main():
def params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror):
if mirror:
m = 1
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0,
540.0 - psi, 0, 0, 1.0)
else:
m = 0
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0,
360.0 - psi, 0, 0, 1.0)
return alpha, sx, sy, m
progname = os.path.basename(sys.argv[0])
usage = progname + " prj_stack --ave2D= --var2D= --ave3D= --var3D= --img_per_grp= --fl=15. --aa=0.01 --sym=symmetry --CTF"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--output_dir",
type="string",
default="./",
help="output directory")
parser.add_option("--ave2D",
type="string",
default=False,
help="write to the disk a stack of 2D averages")
parser.add_option("--var2D",
type="string",
default=False,
help="write to the disk a stack of 2D variances")
parser.add_option("--ave3D",
type="string",
default=False,
help="write to the disk reconstructed 3D average")
parser.add_option("--var3D",
type="string",
default=False,
help="compute 3D variability (time consuming!)")
parser.add_option("--img_per_grp",
type="int",
default=10,
help="number of neighbouring projections")
parser.add_option("--no_norm",
action="store_true",
default=False,
help="do not use normalization")
#parser.add_option("--radius", type="int" , default=-1 , help="radius for 3D variability" )
parser.add_option("--npad",
type="int",
default=2,
help="number of time to pad the original images")
parser.add_option("--sym", type="string", default="c1", help="symmetry")
parser.add_option(
"--fl",
type="float",
default=0.0,
help=
"cutoff freqency in absolute frequency (0.0-0.5). (Default - no filtration)"
)
parser.add_option(
"--aa",
type="float",
default=0.0,
help=
"fall off of the filter. Put 0.01 if user has no clue about falloff (Default - no filtration)"
)
parser.add_option("--CTF",
action="store_true",
default=False,
help="use CFT correction")
parser.add_option("--VERBOSE",
action="store_true",
default=False,
help="Long output for debugging")
#parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
#parser.add_option("--radiuspca", type="int" , default=-1 , help="radius for PCA" )
#parser.add_option("--iter", type="int" , default=40 , help="maximum number of iterations (stop criterion of reconstruction process)" )
#parser.add_option("--abs", type="float" , default=0.0 , help="minimum average absolute change of voxels' values (stop criterion of reconstruction process)" )
#parser.add_option("--squ", type="float" , default=0.0 , help="minimum average squared change of voxels' values (stop criterion of reconstruction process)" )
parser.add_option(
"--VAR",
action="store_true",
default=False,
help="stack on input consists of 2D variances (Default False)")
parser.add_option(
"--decimate",
type="float",
default=1.0,
help=
"image decimate rate, a number larger (expand image) or less (shrink image) than 1. default is 1"
)
parser.add_option(
"--window",
type="int",
default=0,
help=
"reduce images to a small image size without changing pixel_size. Default value is zero."
)
#parser.add_option("--SND", action="store_true", default=False, help="compute squared normalized differences (Default False)")
parser.add_option(
"--nvec",
type="int",
default=0,
help="number of eigenvectors, default = 0 meaning no PCA calculated")
parser.add_option(
"--symmetrize",
action="store_true",
default=False,
help="Prepare input stack for handling symmetry (Default False)")
(options, args) = parser.parse_args()
#####
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, mpi_recv, MPI_COMM_WORLD
from mpi import mpi_barrier, mpi_reduce, mpi_bcast, mpi_send, MPI_FLOAT, MPI_SUM, MPI_INT, MPI_MAX
from applications import MPI_start_end
from reconstruction import recons3d_em, recons3d_em_MPI
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import print_begin_msg, print_end_msg, print_msg
from utilities import read_text_row, get_image, get_im
from utilities import bcast_EMData_to_all, bcast_number_to_all
from utilities import get_symt
# This is code for handling symmetries by the above program. To be incorporated. PAP 01/27/2015
from EMAN2db import db_open_dict
# Set up global variables related to bdb cache
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
# Set up global variables related to ERROR function
global_def.BATCH = True
# detect if program is running under MPI
RUNNING_UNDER_MPI = "OMPI_COMM_WORLD_SIZE" in os.environ
if RUNNING_UNDER_MPI:
global_def.MPI = True
if options.symmetrize:
if RUNNING_UNDER_MPI:
try:
sys.argv = mpi_init(len(sys.argv), sys.argv)
try:
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
if (number_of_proc > 1):
ERROR(
"Cannot use more than one CPU for symmetry prepration",
"sx3dvariability", 1)
except:
pass
except:
pass
if options.output_dir != "./" and not os.path.exists(
options.output_dir):
os.mkdir(options.output_dir)
# Input
#instack = "Clean_NORM_CTF_start_wparams.hdf"
#instack = "bdb:data"
from logger import Logger, BaseLogger_Files
if os.path.exists(os.path.join(options.output_dir, "log.txt")):
os.remove(os.path.join(options.output_dir, "log.txt"))
log_main = Logger(BaseLogger_Files())
log_main.prefix = os.path.join(options.output_dir, "./")
instack = args[0]
sym = options.sym.lower()
if (sym == "c1"):
ERROR("There is no need to symmetrize stack for C1 symmetry",
"sx3dvariability", 1)
line = ""
for a in sys.argv:
line += " " + a
log_main.add(line)
if (instack[:4] != "bdb:"):
if output_dir == "./": stack = "bdb:data"
else: stack = "bdb:" + options.output_dir + "/data"
delete_bdb(stack)
junk = cmdexecute("sxcpy.py " + instack + " " + stack)
else:
stack = instack
qt = EMUtil.get_all_attributes(stack, 'xform.projection')
na = len(qt)
ts = get_symt(sym)
ks = len(ts)
angsa = [None] * na
for k in xrange(ks):
#Qfile = "Q%1d"%k
if options.output_dir != "./":
Qfile = os.path.join(options.output_dir, "Q%1d" % k)
else:
Qfile = os.path.join(options.output_dir, "Q%1d" % k)
#delete_bdb("bdb:Q%1d"%k)
delete_bdb("bdb:" + Qfile)
#junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
junk = cmdexecute("e2bdb.py " + stack + " --makevstack=bdb:" +
Qfile)
#DB = db_open_dict("bdb:Q%1d"%k)
DB = db_open_dict("bdb:" + Qfile)
for i in xrange(na):
ut = qt[i] * ts[k]
DB.set_attr(i, "xform.projection", ut)
#bt = ut.get_params("spider")
#angsa[i] = [round(bt["phi"],3)%360.0, round(bt["theta"],3)%360.0, bt["psi"], -bt["tx"], -bt["ty"]]
#write_text_row(angsa, 'ptsma%1d.txt'%k)
#junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
#junk = cmdexecute("sxheader.py bdb:Q%1d --params=xform.projection --import=ptsma%1d.txt"%(k,k))
DB.close()
if options.output_dir == "./": delete_bdb("bdb:sdata")
else: delete_bdb("bdb:" + options.output_dir + "/" + "sdata")
#junk = cmdexecute("e2bdb.py . --makevstack=bdb:sdata --filt=Q")
sdata = "bdb:" + options.output_dir + "/" + "sdata"
print(sdata)
junk = cmdexecute("e2bdb.py " + options.output_dir +
" --makevstack=" + sdata + " --filt=Q")
#junk = cmdexecute("ls EMAN2DB/sdata*")
#a = get_im("bdb:sdata")
a = get_im(sdata)
a.set_attr("variabilitysymmetry", sym)
#a.write_image("bdb:sdata")
a.write_image(sdata)
else:
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 1:
stack = args[0]
else:
print(("usage: " + usage))
print(("Please run '" + progname + " -h' for detailed options"))
return 1
t0 = time()
# obsolete flags
options.MPI = True
options.nvec = 0
options.radiuspca = -1
options.iter = 40
options.abs = 0.0
options.squ = 0.0
if options.fl > 0.0 and options.aa == 0.0:
ERROR("Fall off has to be given for the low-pass filter",
"sx3dvariability", 1, myid)
if options.VAR and options.SND:
ERROR("Only one of var and SND can be set!", "sx3dvariability",
myid)
exit()
if options.VAR and (options.ave2D or options.ave3D or options.var2D):
ERROR(
"When VAR is set, the program cannot output ave2D, ave3D or var2D",
"sx3dvariability", 1, myid)
exit()
#if options.SND and (options.ave2D or options.ave3D):
# ERROR("When SND is set, the program cannot output ave2D or ave3D", "sx3dvariability", 1, myid)
# exit()
if options.nvec > 0:
ERROR("PCA option not implemented", "sx3dvariability", 1, myid)
exit()
if options.nvec > 0 and options.ave3D == None:
ERROR("When doing PCA analysis, one must set ave3D",
"sx3dvariability",
myid=myid)
exit()
import string
options.sym = options.sym.lower()
# if global_def.CACHE_DISABLE:
# from utilities import disable_bdb_cache
# disable_bdb_cache()
# global_def.BATCH = True
if myid == main_node:
if options.output_dir != "./" and not os.path.exists(
options.output_dir):
os.mkdir(options.output_dir)
img_per_grp = options.img_per_grp
nvec = options.nvec
radiuspca = options.radiuspca
from logger import Logger, BaseLogger_Files
#if os.path.exists(os.path.join(options.output_dir, "log.txt")): os.remove(os.path.join(options.output_dir, "log.txt"))
log_main = Logger(BaseLogger_Files())
log_main.prefix = os.path.join(options.output_dir, "./")
if myid == main_node:
line = ""
for a in sys.argv:
line += " " + a
log_main.add(line)
log_main.add("-------->>>Settings given by all options<<<-------")
log_main.add("instack :" + stack)
log_main.add("output_dir :" + options.output_dir)
log_main.add("var3d :" + options.var3D)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
#print_begin_msg("sx3dvariability")
msg = "sx3dvariability"
log_main.add(msg)
print(line, msg)
msg = ("%-70s: %s\n" % ("Input stack", stack))
log_main.add(msg)
print(line, msg)
symbaselen = 0
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
if options.sym != "c1":
imgdata = get_im(stack)
try:
i = imgdata.get_attr("variabilitysymmetry").lower()
if (i != options.sym):
ERROR(
"The symmetry provided does not agree with the symmetry of the input stack",
"sx3dvariability",
myid=myid)
except:
ERROR(
"Input stack is not prepared for symmetry, please follow instructions",
"sx3dvariability",
myid=myid)
from utilities import get_symt
i = len(get_symt(options.sym))
if ((nima / i) * i != nima):
ERROR(
"The length of the input stack is incorrect for symmetry processing",
"sx3dvariability",
myid=myid)
symbaselen = nima / i
else:
symbaselen = nima
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
Tracker = {}
Tracker["total_stack"] = nima
if options.decimate == 1.:
if options.window != 0:
nx = options.window
ny = options.window
else:
if options.window == 0:
nx = int(nx * options.decimate)
ny = int(ny * options.decimate)
else:
nx = int(options.window * options.decimate)
ny = nx
Tracker["nx"] = nx
Tracker["ny"] = ny
Tracker["nz"] = nx
symbaselen = bcast_number_to_all(symbaselen)
if radiuspca == -1: radiuspca = nx / 2 - 2
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = "%-70s: %d\n" % ("Number of projection", nima)
log_main.add(msg)
print(line, msg)
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
"""
if options.SND:
from projection import prep_vol, prgs
from statistics import im_diff
from utilities import get_im, model_circle, get_params_proj, set_params_proj
from utilities import get_ctf, generate_ctf
from filter import filt_ctf
imgdata = EMData.read_images(stack, range(img_begin, img_end))
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
bcast_EMData_to_all(vol, myid)
volft, kb = prep_vol(vol)
mask = model_circle(nx/2-2, nx, ny)
varList = []
for i in xrange(img_begin, img_end):
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[i-img_begin])
ref_prj = prgs(volft, kb, [phi, theta, psi, -s2x, -s2y])
if options.CTF:
ctf_params = get_ctf(imgdata[i-img_begin])
ref_prj = filt_ctf(ref_prj, generate_ctf(ctf_params))
diff, A, B = im_diff(ref_prj, imgdata[i-img_begin], mask)
diff2 = diff*diff
set_params_proj(diff2, [phi, theta, psi, s2x, s2y])
varList.append(diff2)
mpi_barrier(MPI_COMM_WORLD)
"""
if options.VAR:
#varList = EMData.read_images(stack, range(img_begin, img_end))
varList = []
this_image = EMData()
for index_of_particle in xrange(img_begin, img_end):
this_image.read_image(stack, index_of_particle)
varList.append(
image_decimate_window_xform_ctf(this_image,
options.decimate,
options.window,
options.CTF))
else:
from utilities import bcast_number_to_all, bcast_list_to_all, send_EMData, recv_EMData
from utilities import set_params_proj, get_params_proj, params_3D_2D, get_params2D, set_params2D, compose_transform2
from utilities import model_blank, nearest_proj, model_circle
from applications import pca
from statistics import avgvar, avgvar_ctf, ccc
from filter import filt_tanl
from morphology import threshold, square_root
from projection import project, prep_vol, prgs
from sets import Set
if myid == main_node:
t1 = time()
proj_angles = []
aveList = []
tab = EMUtil.get_all_attributes(stack, 'xform.projection')
for i in xrange(nima):
t = tab[i].get_params('spider')
phi = t['phi']
theta = t['theta']
psi = t['psi']
x = theta
if x > 90.0: x = 180.0 - x
x = x * 10000 + psi
proj_angles.append([x, t['phi'], t['theta'], t['psi'], i])
t2 = time()
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = "%-70s: %d\n" % ("Number of neighboring projections",
img_per_grp)
log_main.add(msg)
print(line, msg)
msg = "...... Finding neighboring projections\n"
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
msg = "Number of images per group: %d" % img_per_grp
log_main.add(msg)
print(line, msg)
msg = "Now grouping projections"
log_main.add(msg)
print(line, msg)
proj_angles.sort()
proj_angles_list = [0.0] * (nima * 4)
if myid == main_node:
for i in xrange(nima):
proj_angles_list[i * 4] = proj_angles[i][1]
proj_angles_list[i * 4 + 1] = proj_angles[i][2]
proj_angles_list[i * 4 + 2] = proj_angles[i][3]
proj_angles_list[i * 4 + 3] = proj_angles[i][4]
proj_angles_list = bcast_list_to_all(proj_angles_list, myid,
main_node)
proj_angles = []
for i in xrange(nima):
proj_angles.append([
proj_angles_list[i * 4], proj_angles_list[i * 4 + 1],
proj_angles_list[i * 4 + 2],
int(proj_angles_list[i * 4 + 3])
])
del proj_angles_list
proj_list, mirror_list = nearest_proj(proj_angles, img_per_grp,
range(img_begin, img_end))
all_proj = Set()
for im in proj_list:
for jm in im:
all_proj.add(proj_angles[jm][3])
all_proj = list(all_proj)
if options.VERBOSE:
print("On node %2d, number of images needed to be read = %5d" %
(myid, len(all_proj)))
index = {}
for i in xrange(len(all_proj)):
index[all_proj[i]] = i
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("%-70s: %.2f\n" %
("Finding neighboring projections lasted [s]",
time() - t2))
log_main.add(msg)
print(msg)
msg = ("%-70s: %d\n" %
("Number of groups processed on the main node",
len(proj_list)))
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
print("Grouping projections took: ", (time() - t2) / 60,
"[min]")
print("Number of groups on main node: ", len(proj_list))
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("...... calculating the stack of 2D variances \n")
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
print("Now calculating the stack of 2D variances")
proj_params = [0.0] * (nima * 5)
aveList = []
varList = []
if nvec > 0:
eigList = [[] for i in xrange(nvec)]
if options.VERBOSE:
print("Begin to read images on processor %d" % (myid))
ttt = time()
#imgdata = EMData.read_images(stack, all_proj)
imgdata = []
for index_of_proj in xrange(len(all_proj)):
#img = EMData()
#img.read_image(stack, all_proj[index_of_proj])
dmg = image_decimate_window_xform_ctf(
get_im(stack, all_proj[index_of_proj]), options.decimate,
options.window, options.CTF)
#print dmg.get_xsize(), "init"
imgdata.append(dmg)
if options.VERBOSE:
print("Reading images on processor %d done, time = %.2f" %
(myid, time() - ttt))
print("On processor %d, we got %d images" %
(myid, len(imgdata)))
mpi_barrier(MPI_COMM_WORLD)
'''
imgdata2 = EMData.read_images(stack, range(img_begin, img_end))
if options.fl > 0.0:
for k in xrange(len(imgdata2)):
imgdata2[k] = filt_tanl(imgdata2[k], options.fl, options.aa)
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata2, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata2, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
if myid == main_node:
vol.write_image("vol_ctf.hdf")
print_msg("Writing to the disk volume reconstructed from averages as : %s\n"%("vol_ctf.hdf"))
del vol, imgdata2
mpi_barrier(MPI_COMM_WORLD)
'''
from applications import prepare_2d_forPCA
from utilities import model_blank
for i in xrange(len(proj_list)):
ki = proj_angles[proj_list[i][0]][3]
if ki >= symbaselen: continue
mi = index[ki]
phiM, thetaM, psiM, s2xM, s2yM = get_params_proj(imgdata[mi])
grp_imgdata = []
for j in xrange(img_per_grp):
mj = index[proj_angles[proj_list[i][j]][3]]
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[mj])
alpha, sx, sy, mirror = params_3D_2D_NEW(
phi, theta, psi, s2x, s2y, mirror_list[i][j])
if thetaM <= 90:
if mirror == 0:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, phiM - phi, 0.0, 0.0, 1.0)
else:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, 180 - (phiM - phi), 0.0,
0.0, 1.0)
else:
if mirror == 0:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, -(phiM - phi), 0.0, 0.0,
1.0)
else:
alpha, sx, sy, scale = compose_transform2(
alpha, sx, sy, 1.0, -(180 - (phiM - phi)), 0.0,
0.0, 1.0)
set_params2D(imgdata[mj], [alpha, sx, sy, mirror, 1.0])
grp_imgdata.append(imgdata[mj])
#print grp_imgdata[j].get_xsize(), imgdata[mj].get_xsize()
if not options.no_norm:
#print grp_imgdata[j].get_xsize()
mask = model_circle(nx / 2 - 2, nx, nx)
for k in xrange(img_per_grp):
ave, std, minn, maxx = Util.infomask(
grp_imgdata[k], mask, False)
grp_imgdata[k] -= ave
grp_imgdata[k] /= std
del mask
if options.fl > 0.0:
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2 * nx
ny2 = 2 * ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d(
fft(
filt_tanl(
filt_ctf(
fft(
pad(grp_imgdata[k], nx2, ny2,
1, 0.0)),
grp_imgdata[k].get_attr("ctf"),
binary=1), options.fl,
options.aa)), nx, ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
for k in xrange(img_per_grp):
grp_imgdata[k] = filt_tanl(grp_imgdata[k],
options.fl, options.aa)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
from utilities import pad, read_text_file
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2 * nx
ny2 = 2 * ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d(
fft(
filt_ctf(fft(
pad(grp_imgdata[k], nx2, ny2, 1, 0.0)),
grp_imgdata[k].get_attr("ctf"),
binary=1)), nx, ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
'''
if i < 10 and myid == main_node:
for k in xrange(10):
grp_imgdata[k].write_image("grp%03d.hdf"%i, k)
'''
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("pp.hdf", pp)
"""
ave, grp_imgdata = prepare_2d_forPCA(grp_imgdata)
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("qq.hdf", pp)
"""
var = model_blank(nx, ny)
for q in grp_imgdata:
Util.add_img2(var, q)
Util.mul_scalar(var, 1.0 / (len(grp_imgdata) - 1))
# Switch to std dev
var = square_root(threshold(var))
#if options.CTF: ave, var = avgvar_ctf(grp_imgdata, mode="a")
#else: ave, var = avgvar(grp_imgdata, mode="a")
"""
if myid == main_node:
ave.write_image("avgv.hdf",i)
var.write_image("varv.hdf",i)
"""
set_params_proj(ave, [phiM, thetaM, 0.0, 0.0, 0.0])
set_params_proj(var, [phiM, thetaM, 0.0, 0.0, 0.0])
aveList.append(ave)
varList.append(var)
if options.VERBOSE:
print("%5.2f%% done on processor %d" %
(i * 100.0 / len(proj_list), myid))
if nvec > 0:
eig = pca(input_stacks=grp_imgdata,
subavg="",
mask_radius=radiuspca,
nvec=nvec,
incore=True,
shuffle=False,
genbuf=True)
for k in xrange(nvec):
set_params_proj(eig[k], [phiM, thetaM, 0.0, 0.0, 0.0])
eigList[k].append(eig[k])
"""
if myid == 0 and i == 0:
for k in xrange(nvec):
eig[k].write_image("eig.hdf", k)
"""
del imgdata
# To this point, all averages, variances, and eigenvectors are computed
if options.ave2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node:
for im in xrange(len(aveList)):
aveList[im].write_image(
os.path.join(options.output_dir,
options.ave2D), km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i,
SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im + i + 70000)
"""
nm = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('pix_err', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
"""
tmpvol = fpol(ave, Tracker["nx"],
Tracker["nx"], 1)
tmpvol.write_image(
os.path.join(options.output_dir,
options.ave2D), km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node, im + myid + 70000)
"""
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
members = aveList[im].get_attr('pix_err')
mpi_send(members, len(members), MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
"""
if options.ave3D:
from fundamentals import fpol
if options.VERBOSE:
print("Reconstructing 3D average volume")
ave3D = recons3d_4nn_MPI(myid,
aveList,
symmetry=options.sym,
npad=options.npad)
bcast_EMData_to_all(ave3D, myid)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
ave3D = fpol(ave3D, Tracker["nx"], Tracker["nx"],
Tracker["nx"])
ave3D.write_image(
os.path.join(options.output_dir, options.ave3D))
msg = ("%-70s: %s\n" % (
"Writing to the disk volume reconstructed from averages as",
options.ave3D))
log_main.add(msg)
print(line, msg)
del ave, var, proj_list, stack, phi, theta, psi, s2x, s2y, alpha, sx, sy, mirror, aveList
if nvec > 0:
for k in xrange(nvec):
if options.VERBOSE:
print("Reconstruction eigenvolumes", k)
cont = True
ITER = 0
mask2d = model_circle(radiuspca, nx, nx)
while cont:
#print "On node %d, iteration %d"%(myid, ITER)
eig3D = recons3d_4nn_MPI(myid,
eigList[k],
symmetry=options.sym,
npad=options.npad)
bcast_EMData_to_all(eig3D, myid, main_node)
if options.fl > 0.0:
eig3D = filt_tanl(eig3D, options.fl, options.aa)
if myid == main_node:
eig3D.write_image(
os.path.join(options.outpout_dir,
"eig3d_%03d.hdf" % (k, ITER)))
Util.mul_img(eig3D,
model_circle(radiuspca, nx, nx, nx))
eig3Df, kb = prep_vol(eig3D)
del eig3D
cont = False
icont = 0
for l in xrange(len(eigList[k])):
phi, theta, psi, s2x, s2y = get_params_proj(
eigList[k][l])
proj = prgs(eig3Df, kb,
[phi, theta, psi, s2x, s2y])
cl = ccc(proj, eigList[k][l], mask2d)
if cl < 0.0:
icont += 1
cont = True
eigList[k][l] *= -1.0
u = int(cont)
u = mpi_reduce([u], 1, MPI_INT, MPI_MAX, main_node,
MPI_COMM_WORLD)
icont = mpi_reduce([icont], 1, MPI_INT, MPI_SUM,
main_node, MPI_COMM_WORLD)
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S",
localtime()) + " =>"
u = int(u[0])
msg = (" Eigenvector: ", k, " number changed ",
int(icont[0]))
log_main.add(msg)
print(line, msg)
else:
u = 0
u = bcast_number_to_all(u, main_node)
cont = bool(u)
ITER += 1
del eig3Df, kb
mpi_barrier(MPI_COMM_WORLD)
del eigList, mask2d
if options.ave3D: del ave3D
if options.var2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node:
for im in xrange(len(varList)):
tmpvol = fpol(varList[im], Tracker["nx"],
Tracker["nx"], 1)
tmpvol.write_image(
os.path.join(options.output_dir,
options.var2D), km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i,
SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im + i + 70000)
tmpvol = fpol(ave, Tracker["nx"],
Tracker["nx"], 1)
tmpvol.write_image(
os.path.join(options.output_dir,
options.var2D, km))
km += 1
else:
mpi_send(len(varList), 1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
for im in xrange(len(varList)):
send_EMData(varList[im], main_node, im + myid +
70000) # What with the attributes??
mpi_barrier(MPI_COMM_WORLD)
if options.var3D:
if myid == main_node and options.VERBOSE:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("Reconstructing 3D variability volume")
log_main.add(msg)
print(line, msg)
t6 = time()
# radiusvar = options.radius
# if( radiusvar < 0 ): radiusvar = nx//2 -3
res = recons3d_4nn_MPI(myid,
varList,
symmetry=options.sym,
npad=options.npad)
#res = recons3d_em_MPI(varList, vol_stack, options.iter, radiusvar, options.abs, True, options.sym, options.squ)
if myid == main_node:
from fundamentals import fpol
res = fpol(res, Tracker["nx"], Tracker["nx"], Tracker["nx"])
res.write_image(os.path.join(options.output_dir,
options.var3D))
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("%-70s: %.2f\n" %
("Reconstructing 3D variability took [s]", time() - t6))
log_main.add(msg)
print(line, msg)
if options.VERBOSE:
print("Reconstruction took: %.2f [min]" %
((time() - t6) / 60))
if myid == main_node:
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("%-70s: %.2f\n" %
("Total time for these computations [s]", time() - t0))
print(line, msg)
log_main.add(msg)
if options.VERBOSE:
print("Total time for these computations: %.2f [min]" %
((time() - t0) / 60))
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
msg = ("sx3dvariability")
print(line, msg)
log_main.add(msg)
from mpi import mpi_finalize
mpi_finalize()
if RUNNING_UNDER_MPI:
global_def.MPI = False
global_def.BATCH = False
def main():
from utilities import get_input_from_string
progname = os.path.basename(sys.argv[0])
usage = progname + " stack output_average --radius=particle_radius --xr=xr --yr=yr --ts=ts --thld_err=thld_err --num_ali=num_ali --fl=fl --aa=aa --CTF --verbose --stables"
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--radius", type="int", default=-1, help=" particle radius for alignment")
parser.add_option("--xr", type="string" , default="2 1", help="range for translation search in x direction, search is +/xr (default 2,1)")
parser.add_option("--yr", type="string" , default="-1", help="range for translation search in y direction, search is +/yr (default = same as xr)")
parser.add_option("--ts", type="string" , default="1 0.5", help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional (default: 1,0.5)")
parser.add_option("--thld_err", type="float", default=0.75, help="threshld of pixel error (default = 0.75)")
parser.add_option("--num_ali", type="int", default=5, help="number of alignments performed for stability (default = 5)")
parser.add_option("--maxit", type="int", default=30, help="number of iterations for each xr (default = 30)")
parser.add_option("--fl", type="float" , default=0.3, help="cut-off frequency of hyperbolic tangent low-pass Fourier filter (default = 0.3)")
parser.add_option("--aa", type="float" , default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter (default = 0.2)")
parser.add_option("--CTF", action="store_true", default=False, help="Use CTF correction during the alignment ")
parser.add_option("--verbose", action="store_true", default=False, help="print individual pixel error (default = False)")
parser.add_option("--stables", action="store_true", default=False, help="output the stable particles number in file (default = False)")
parser.add_option("--method", type="string" , default=" ", help="SHC (standard method is default when flag is ommitted)")
(options, args) = parser.parse_args()
if len(args) != 1 and len(args) != 2:
print "usage: " + usage
print "Please run '" + progname + " -h' for detailed options"
else:
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
from applications import within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import write_text_file, write_text_row
global_def.BATCH = True
xrng = get_input_from_string(options.xr)
if options.yr == "-1": yrng = xrng
else : yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
class_data = EMData.read_images(args[0])
nx = class_data[0].get_xsize()
ou = options.radius
num_ali = options.num_ali
if ou == -1: ou = nx/2-2
from utilities import model_circle, get_params2D, set_params2D
mask = model_circle(ou, nx, nx)
if options.CTF :
from filter import filt_ctf
for im in xrange(len(class_data)):
# Flip phases
class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
im.set_attr("previousmax", -1.0e10)
try:
t = im.get_attr("xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
all_ali_params = []
for ii in xrange(num_ali):
ali_params = []
if options.verbose:
ALPHA = []
SX = []
SY = []
MIRROR = []
if( xrng[0] == 0.0 and yrng[0] == 0.0 ):
avet = ali2d_ras(class_data, randomize = True, ir = 1, ou = ou, rs = 1, step = 1.0, dst = 90.0, \
maxit = options.maxit, check_mirror = True, FH=options.fl, FF=options.aa)
else:
avet = within_group_refinement(class_data, mask, True, 1, ou, 1, xrng, yrng, step, 90.0, \
maxit = options.maxit, FH=options.fl, FF=options.aa, method = options.method)
from utilities import info
#print " avet ",info(avet)
for im in class_data:
alpha, sx, sy, mirror, scale = get_params2D(im)
ali_params.extend([alpha, sx, sy, mirror])
if options.verbose:
ALPHA.append(alpha)
SX.append(sx)
SY.append(sy)
MIRROR.append(mirror)
all_ali_params.append(ali_params)
if options.verbose:
write_text_file([ALPHA, SX, SY, MIRROR], "ali_params_run_%d"%ii)
"""
avet = class_data[0]
from utilities import read_text_file
all_ali_params = []
for ii in xrange(5):
temp = read_text_file( "ali_params_run_%d"%ii,-1)
uuu = []
for k in xrange(len(temp[0])):
uuu.extend([temp[0][k],temp[1][k],temp[2][k],temp[3][k]])
all_ali_params.append(uuu)
"""
stable_set, mir_stab_rate, pix_err = multi_align_stability(all_ali_params, 0.0, 10000.0, options.thld_err, options.verbose, 2*ou+1)
print "%4s %20s %20s %20s %30s %6.2f"%("", "Size of set", "Size of stable set", "Mirror stab rate", "Pixel error prior to pruning the set above threshold of",options.thld_err)
print "Average stat: %10d %20d %20.2f %15.2f"%( len(class_data), len(stable_set), mir_stab_rate, pix_err)
if( len(stable_set) > 0):
if options.stables:
stab_mem = [[0,0.0,0] for j in xrange(len(stable_set))]
for j in xrange(len(stable_set)): stab_mem[j] = [int(stable_set[j][1]), stable_set[j][0], j]
write_text_row(stab_mem, "stable_particles.txt")
stable_set_id = []
particle_pixerr = []
for s in stable_set:
stable_set_id.append(s[1])
particle_pixerr.append(s[0])
from fundamentals import rot_shift2D
avet.to_zero()
l = -1
print "average parameters: angle, x-shift, y-shift, mirror"
for j in stable_set_id:
l += 1
print " %4d %4d %12.2f %12.2f %12.2f %1d"%(l,j, stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], int(stable_set[l][2][3]))
avet += rot_shift2D(class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3] )
avet /= (l+1)
avet.set_attr('members', stable_set_id)
avet.set_attr('pix_err', pix_err)
avet.set_attr('pixerr', particle_pixerr)
avet.write_image(args[1])
global_def.BATCH = False
def main():
import sys
import os
import math
import random
import pyemtbx.options
import time
from random import random, seed, randint
from optparse import OptionParser
progname = os.path.basename(sys.argv[0])
usage = progname + """ [options] <inputfile> <outputfile>
Forms chains of 2D images based on their similarities.
Functionality:
4. Order a 2-D stack of image based on pair-wise similarity (computed as a cross-correlation coefficent).
Options 1-3 require image stack to be aligned. The program will apply orientation parameters if present in headers.
The ways to use the program:
4.1 Use option initial to specify which image will be used as an initial seed to form the chain.
sxprocess.py input_stack.hdf output_stack.hdf --initial=23 --radius=25
4.2 If options initial is omitted, the program will determine which image best serves as initial seed to form the chain
sxprocess.py input_stack.hdf output_stack.hdf --radius=25
4.3 Use option circular to form a circular chain.
sxprocess.py input_stack.hdf output_stack.hdf --circular--radius=25
4.4 New circular code based on pairwise alignments
sxprocess.py aclf.hdf chain.hdf circle.hdf --align --radius=25 --xr=2 --pairwiseccc=lcc.txt
4.5 Circular ordering based on pairwise alignments
sxprocess.py vols.hdf chain.hdf mask.hdf --dd --radius=25
"""
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--dd", action="store_true", help="Circular ordering without adjustment of orientations", default=False)
parser.add_option("--circular", action="store_true", help="Select circular ordering (first image has to be similar to the last)", default=False)
parser.add_option("--align", action="store_true", help="Compute all pairwise alignments and for the table of their similarities find the best chain", default=False)
parser.add_option("--initial", type="int", default=-1, help="Specifies which image will be used as an initial seed to form the chain. (default = 0, means the first image)")
parser.add_option("--radius", type="int", default=-1, help="Radius of a circular mask for similarity based ordering")
# import params for 2D alignment
parser.add_option("--ou", type="int", default=-1, help="outer radius for 2D alignment < nx/2-1 (set to the radius of the particle)")
parser.add_option("--xr", type="int", default=0, help="range for translation search in x direction, search is +/xr (0)")
parser.add_option("--yr", type="int", default=0, help="range for translation search in y direction, search is +/yr (0)")
#parser.add_option("--nomirror", action="store_true", default=False, help="Disable checking mirror orientations of images (default False)")
parser.add_option("--pairwiseccc", type="string", default= None, help="Input/output pairwise ccc file")
(options, args) = parser.parse_args()
global_def.BATCH = True
if options.dd:
nargs = len(args)
if nargs != 3:
print "must provide name of input and two output files!"
return
stack = args[0]
new_stack = args[1]
from utilities import model_circle
from statistics import ccc
from statistics import mono
lend = EMUtil.get_image_count(stack)
lccc = [None]*(lend*(lend-1)/2)
for i in xrange(lend-1):
v1 = get_im( stack, i )
if( i == 0 and nargs == 2):
nx = v1.get_xsize()
ny = v1.get_ysize()
nz = v1.get_ysize()
if options.ou < 1 : radius = nx//2-2
else: radius = options.ou
mask = model_circle(radius, nx, ny, nz)
else:
mask = get_im(args[2])
for j in xrange(i+1, lend):
lccc[mono(i,j)] = [ccc(v1, get_im( stack, j ), mask), 0]
order = tsp(lccc)
if(len(order) != lend):
print " problem with data length"
from sys import exit
exit()
print "Total sum of cccs :",TotalDistance(order, lccc)
print "ordering :",order
for i in xrange(lend): get_im(stack, order[i]).write_image( new_stack, i )
elif options.align:
nargs = len(args)
if nargs != 3:
print "must provide name of input and two output files!"
return
from utilities import get_params2D, model_circle
from fundamentals import rot_shift2D
from statistics import ccc
from time import time
from alignment import align2d, align2d_scf
from multi_shc import mult_transform
stack = args[0]
new_stack = args[1]
d = EMData.read_images(stack)
"""
# will align anyway
try:
ttt = d[0].get_attr('xform.params2d')
for i in xrange(len(d)):
alpha, sx, sy, mirror, scale = get_params2D(d[i])
d[i] = rot_shift2D(d[i], alpha, sx, sy, mirror)
except:
pass
"""
nx = d[0].get_xsize()
ny = d[0].get_ysize()
if options.ou < 1 : radius = nx//2-2
else: radius = options.ou
mask = model_circle(radius, nx, ny)
if(options.xr < 0): xrng = 0
else: xrng = options.xr
if(options.yr < 0): yrng = xrng
else: yrng = options.yr
initial = max(options.initial, 0)
from statistics import mono
lend = len(d)
lccc = [None]*(lend*(lend-1)/2)
from utilities import read_text_row
if options.pairwiseccc == None or not os.path.exists(options.pairwiseccc) :
st = time()
for i in xrange(lend-1):
for j in xrange(i+1, lend):
# j>i meaning mono entry (i,j) or (j,i) indicates T i->j (from smaller index to larger)
#alpha, sx, sy, mir, peak = align2d(d[i],d[j], xrng, yrng, step=options.ts, first_ring=options.ir, last_ring=radius, mode = "F")
alpha, sx, sy, mir, peak = align2d_scf(d[i],d[j], xrng, yrng, ou=radius)
lccc[mono(i,j)] = [ccc(d[j], rot_shift2D(d[i], alpha, sx, sy, mir, 1.0), mask), alpha, sx, sy, mir]
#print " %4d %10.1f"%(i,time()-st)
if(not os.path.exists(options.pairwiseccc)):
from utilities import write_text_row
write_text_row([[initial,0,0,0,0]]+lccc,options.pairwiseccc)
elif(os.path.exists(options.pairwiseccc)):
lccc = read_text_row(options.pairwiseccc)
initial = int(lccc[0][0] + 0.1)
del lccc[0]
for i in xrange(len(lccc)):
T = Transform({"type":"2D","alpha":lccc[i][1],"tx":lccc[i][2],"ty":lccc[i][3],"mirror":int(lccc[i][4]+0.1)})
lccc[i] = [lccc[i][0],T]
tdummy = Transform({"type":"2D"})
maxsum = -1.023
for m in xrange(0,lend):#initial, initial+1):
indc = range( lend )
lsnake = [[m, tdummy, 0.0]]
del indc[m]
lsum = 0.0
while len(indc) > 1:
maxcit = -111.
for i in xrange(len(indc)):
cuc = lccc[mono(indc[i], lsnake[-1][0])][0]
if cuc > maxcit:
maxcit = cuc
qi = indc[i]
# Here we need transformation from the current to the previous,
# meaning indc[i] -> lsnake[-1][0]
T = lccc[mono(indc[i], lsnake[-1][0])][1]
# If direction is from larger to smaller index, the transformation has to be inverted
if( indc[i] > lsnake[-1][0] ): T = T.inverse()
lsnake.append([qi,T, maxcit])
lsum += maxcit
del indc[indc.index(qi)]
T = lccc[mono(indc[-1], lsnake[-1][0])][1]
if( indc[-1] > lsnake[-1][0]): T = T.inverse()
lsnake.append([indc[-1], T, lccc[mono(indc[-1], lsnake[-1][0])][0]])
print " initial image and lsum ",m,lsum
#print lsnake
if(lsum > maxsum):
maxsum = lsum
init = m
snake = [lsnake[i] for i in xrange(lend)]
print " Initial image selected : ",init,maxsum," ",TotalDistance([snake[m][0] for m in xrange(lend)], lccc)
#for q in snake: print q
from copy import deepcopy
trans=deepcopy([snake[i][1] for i in xrange(len(snake))])
print [snake[i][0] for i in xrange(len(snake))]
def main():
import sys
import os
import math
import random
import pyemtbx.options
import time
from random import random, seed, randint
from optparse import OptionParser
progname = os.path.basename(sys.argv[0])
usage = progname + """ [options] <inputfile> <outputfile>
Forms chains of 2D images based on their similarities.
Functionality:
4. Order a 2-D stack of image based on pair-wise similarity (computed as a cross-correlation coefficent).
Options 1-3 require image stack to be aligned. The program will apply orientation parameters if present in headers.
The ways to use the program:
4.1 Use option initial to specify which image will be used as an initial seed to form the chain.
sxprocess.py input_stack.hdf output_stack.hdf --initial=23 --radius=25
4.2 If options initial is omitted, the program will determine which image best serves as initial seed to form the chain
sxprocess.py input_stack.hdf output_stack.hdf --radius=25
4.3 Use option circular to form a circular chain.
sxprocess.py input_stack.hdf output_stack.hdf --circular--radius=25
4.4 New circular code based on pairwise alignments
sxprocess.py aclf.hdf chain.hdf circle.hdf --align --radius=25 --xr=2 --pairwiseccc=lcc.txt
4.5 Circular ordering based on pairwise alignments
sxprocess.py vols.hdf chain.hdf mask.hdf --dd --radius=25
"""
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--dd", action="store_true", help="Circular ordering without adjustment of orientations", default=False)
parser.add_option("--circular", action="store_true", help="Select circular ordering (first image has to be similar to the last)", default=False)
parser.add_option("--align", action="store_true", help="Compute all pairwise alignments and for the table of their similarities find the best chain", default=False)
parser.add_option("--initial", type="int", default=-1, help="Specifies which image will be used as an initial seed to form the chain. (default = 0, means the first image)")
parser.add_option("--radius", type="int", default=-1, help="Radius of a circular mask for similarity based ordering")
# import params for 2D alignment
parser.add_option("--ou", type="int", default=-1, help="outer radius for 2D alignment < nx/2-1 (set to the radius of the particle)")
parser.add_option("--xr", type="int", default=0, help="range for translation search in x direction, search is +/xr (0)")
parser.add_option("--yr", type="int", default=0, help="range for translation search in y direction, search is +/yr (0)")
#parser.add_option("--nomirror", action="store_true", default=False, help="Disable checking mirror orientations of images (default False)")
parser.add_option("--pairwiseccc", type="string", default= None, help="Input/output pairwise ccc file")
(options, args) = parser.parse_args()
global_def.BATCH = True
if options.dd:
nargs = len(args)
if nargs != 3:
print "must provide name of input and two output files!"
return
stack = args[0]
new_stack = args[1]
from utilities import model_circle
from statistics import ccc
from statistics import mono
lend = EMUtil.get_image_count(stack)
lccc = [None]*(lend*(lend-1)/2)
for i in xrange(lend-1):
v1 = get_im( stack, i )
if( i == 0 and nargs == 2):
nx = v1.get_xsize()
ny = v1.get_ysize()
nz = v1.get_ysize()
if options.ou < 1 : radius = nx//2-2
else: radius = options.ou
mask = model_circle(radius, nx, ny, nz)
else:
mask = get_im(args[2])
for j in xrange(i+1, lend):
lccc[mono(i,j)] = [ccc(v1, get_im( stack, j ), mask), 0]
order = tsp(lccc)
if(len(order) != lend):
print " problem with data length"
from sys import exit
exit()
print "Total sum of cccs :",TotalDistance(order, lccc)
print "ordering :",order
for i in xrange(lend): get_im(stack, order[i]).write_image( new_stack, i )
elif options.align:
nargs = len(args)
if nargs != 3:
print "must provide name of input and two output files!"
return
from utilities import get_params2D, model_circle
from fundamentals import rot_shift2D
from statistics import ccc
from time import time
from alignment import align2d, align2d_scf
from multi_shc import mult_transform
stack = args[0]
new_stack = args[1]
d = EMData.read_images(stack)
"""
# will align anyway
try:
ttt = d[0].get_attr('xform.params2d')
for i in xrange(len(d)):
alpha, sx, sy, mirror, scale = get_params2D(d[i])
d[i] = rot_shift2D(d[i], alpha, sx, sy, mirror)
except:
pass
"""
nx = d[0].get_xsize()
ny = d[0].get_ysize()
if options.ou < 1 : radius = nx//2-2
else: radius = options.ou
mask = model_circle(radius, nx, ny)
if(options.xr < 0): xrng = 0
else: xrng = options.xr
if(options.yr < 0): yrng = xrng
else: yrng = options.yr
initial = max(options.initial, 0)
from statistics import mono
lend = len(d)
lccc = [None]*(lend*(lend-1)/2)
from utilities import read_text_row
if options.pairwiseccc == None or not os.path.exists(options.pairwiseccc) :
st = time()
for i in xrange(lend-1):
for j in xrange(i+1, lend):
# j>i meaning mono entry (i,j) or (j,i) indicates T i->j (from smaller index to larger)
#alpha, sx, sy, mir, peak = align2d(d[i],d[j], xrng, yrng, step=options.ts, first_ring=options.ir, last_ring=radius, mode = "F")
alpha, sx, sy, mir, peak = align2d_scf(d[i],d[j], xrng, yrng, ou=radius)
lccc[mono(i,j)] = [ccc(d[j], rot_shift2D(d[i], alpha, sx, sy, mir, 1.0), mask), alpha, sx, sy, mir]
#print " %4d %10.1f"%(i,time()-st)
if(not os.path.exists(options.pairwiseccc)):
from utilities import write_text_row
write_text_row([[initial,0,0,0,0]]+lccc,options.pairwiseccc)
elif(os.path.exists(options.pairwiseccc)):
lccc = read_text_row(options.pairwiseccc)
initial = int(lccc[0][0] + 0.1)
del lccc[0]
for i in xrange(len(lccc)):
T = Transform({"type":"2D","alpha":lccc[i][1],"tx":lccc[i][2],"ty":lccc[i][3],"mirror":int(lccc[i][4]+0.1)})
lccc[i] = [lccc[i][0],T]
tdummy = Transform({"type":"2D"})
maxsum = -1.023
for m in xrange(0,lend):#initial, initial+1):
indc = range( lend )
lsnake = [[m, tdummy, 0.0]]
del indc[m]
lsum = 0.0
while len(indc) > 1:
maxcit = -111.
for i in xrange(len(indc)):
cuc = lccc[mono(indc[i], lsnake[-1][0])][0]
if cuc > maxcit:
maxcit = cuc
qi = indc[i]
# Here we need transformation from the current to the previous,
# meaning indc[i] -> lsnake[-1][0]
T = lccc[mono(indc[i], lsnake[-1][0])][1]
# If direction is from larger to smaller index, the transformation has to be inverted
if( indc[i] > lsnake[-1][0] ): T = T.inverse()
lsnake.append([qi,T, maxcit])
lsum += maxcit
del indc[indc.index(qi)]
T = lccc[mono(indc[-1], lsnake[-1][0])][1]
if( indc[-1] > lsnake[-1][0]): T = T.inverse()
lsnake.append([indc[-1], T, lccc[mono(indc[-1], lsnake[-1][0])][0]])
print " initial image and lsum ",m,lsum
#print lsnake
if(lsum > maxsum):
maxsum = lsum
init = m
snake = [lsnake[i] for i in xrange(lend)]
print " Initial image selected : ",init,maxsum," ",TotalDistance([snake[m][0] for m in xrange(lend)], lccc)
#for q in snake: print q
from copy import deepcopy
trans=deepcopy([snake[i][1] for i in xrange(len(snake))])
print [snake[i][0] for i in xrange(len(snake))]
"""
for m in xrange(lend):
prms = trans[m].get_params("2D")
print " %3d %7.1f %7.1f %7.1f %2d %6.2f"%(snake[m][0], prms["alpha"], prms["tx"], prms["ty"], prms["mirror"], snake[m][2])
"""
for k in xrange(lend-2,0,-1):
T = snake[k][1]
for i in xrange(k+1, lend):
trans[i] = T*trans[i]
# To add - apply all transformations and do the overall centering.
for m in xrange(lend):
prms = trans[m].get_params("2D")
#print " %3d %7.1f %7.1f %7.1f %2d %6.2f"%(snake[m][0], prms["alpha"], prms["tx"], prms["ty"], prms["mirror"], snake[m][2])
#rot_shift2D(d[snake[m][0]], prms["alpha"], prms["tx"], prms["ty"], prms["mirror"]).write_image(new_stack, m)
rot_shift2D(d[snake[m][0]], prms["alpha"], 0.0,0.0, prms["mirror"]).write_image(new_stack, m)
order = tsp(lccc)
if(len(order) != lend):
print " problem with data length"
from sys import exit
exit()
print TotalDistance(order, lccc)
print order
ibeg = order.index(init)
order = [order[(i+ibeg)%lend] for i in xrange(lend)]
print TotalDistance(order, lccc)
print order
snake = [tdummy]
for i in xrange(1,lend):
# Here we need transformation from the current to the previous,
# meaning order[i] -> order[i-1]]
T = lccc[mono(order[i], order[i-1])][1]
# If direction is from larger to smaller index, the transformation has to be inverted
if( order[i] > order[i-1] ): T = T.inverse()
snake.append(T)
assert(len(snake) == lend)
from copy import deepcopy
trans = deepcopy(snake)
for k in xrange(lend-2,0,-1):
T = snake[k]
for i in xrange(k+1, lend):
trans[i] = T*trans[i]
# Try to smooth the angles - complicated, I am afraid one would have to use angles forward and backwards
# and find their average??
# In addition, one would have to recenter them
"""
trms = []
for m in xrange(lend):
prms = trans[m].get_params("2D")
trms.append([prms["alpha"], prms["mirror"]])
for i in xrange(3):
for m in xrange(lend):
mb = (m-1)%lend
me = (m+1)%lend
# angles order mb,m,me
# calculate predicted angles mb->m
"""
for m in xrange(lend):
prms = trans[m].get_params("2D")
#rot_shift2D(d[order[m]], prms["alpha"], prms["tx"], prms["ty"], prms["mirror"]).write_image("metro.hdf", m)
rot_shift2D(d[order[m]], prms["alpha"], 0.0,0.0, prms["mirror"]).write_image(args[2], m)
"""
# This was an effort to get number of loops, inconclusive, to say the least
from numpy import outer, zeros, float32, sqrt
lend = len(d)
cor = zeros(lend,float32)
cor = outer(cor, cor)
for i in xrange(lend): cor[i][i] = 1.0
for i in xrange(lend-1):
for j in xrange(i+1, lend):
cor[i,j] = lccc[mono(i,j)][0]
cor[j,i] = cor[i,j]
lmbd, eigvec = pca(cor)
from utilities import write_text_file
nvec=20
print [lmbd[j] for j in xrange(nvec)]
print " G"
mm = [-1]*lend
for i in xrange(lend): # row
mi = -1.0e23
for j in xrange(nvec):
qt = eigvec[j][i]
if(abs(qt)>mi):
mi = abs(qt)
mm[i] = j
for j in xrange(nvec):
qt = eigvec[j][i]
print round(qt,3), # eigenvector
print mm[i]
print
for j in xrange(nvec):
qt = []
for i in xrange(lend):
if(mm[i] == j): qt.append(i)
if(len(qt)>0): write_text_file(qt,"loop%02d.txt"%j)
"""
"""
print [lmbd[j] for j in xrange(nvec)]
print " B"
mm = [-1]*lend
for i in xrange(lend): # row
mi = -1.0e23
for j in xrange(nvec):
qt = eigvec[j][i]/sqrt(lmbd[j])
if(abs(qt)>mi):
mi = abs(qt)
mm[i] = j
for j in xrange(nvec):
qt = eigvec[j][i]/sqrt(lmbd[j])
print round(qt,3), # eigenvector
print mm[i]
print
"""
"""
lend=3
cor = zeros(lend,float32)
cor = outer(cor, cor)
cor[0][0] =136.77
cor[0][1] = 79.15
cor[0][2] = 37.13
cor[1][0] = 79.15
cor[2][0] = 37.13
cor[1][1] = 50.04
cor[1][2] = 21.65
cor[2][1] = 21.65
cor[2][2] = 13.26
lmbd, eigvec = pca(cor)
print lmbd
print eigvec
for i in xrange(lend): # row
for j in xrange(lend): print eigvec[j][i], # eigenvector
print
print " B"
for i in xrange(lend): # row
for j in xrange(lend): print eigvec[j][i]/sqrt(lmbd[j]), # eigenvector
print
print " G"
for i in xrange(lend): # row
for j in xrange(lend): print eigvec[j][i]*sqrt(lmbd[j]), # eigenvector
print
"""
else:
nargs = len(args)
if nargs != 2:
print "must provide name of input and output file!"
return
from utilities import get_params2D, model_circle
from fundamentals import rot_shift2D
from statistics import ccc
from time import time
from alignment import align2d
from multi_shc import mult_transform
stack = args[0]
new_stack = args[1]
d = EMData.read_images(stack)
try:
ttt = d[0].get_attr('xform.params2d')
for i in xrange(len(d)):
alpha, sx, sy, mirror, scale = get_params2D(d[i])
d[i] = rot_shift2D(d[i], alpha, sx, sy, mirror)
except:
pass
nx = d[0].get_xsize()
ny = d[0].get_ysize()
if options.radius < 1 : radius = nx//2-2
else: radius = options.radius
mask = model_circle(radius, nx, ny)
init = options.initial
if init > -1 :
print " initial image: %d" % init
temp = d[init].copy()
temp.write_image(new_stack, 0)
del d[init]
k = 1
lsum = 0.0
while len(d) > 1:
maxcit = -111.
for i in xrange(len(d)):
cuc = ccc(d[i], temp, mask)
if cuc > maxcit:
maxcit = cuc
qi = i
# print k, maxcit
lsum += maxcit
temp = d[qi].copy()
del d[qi]
temp.write_image(new_stack, k)
k += 1
print lsum
d[0].write_image(new_stack, k)
else:
if options.circular :
print "Using options.circular"
# figure the "best circular" starting image
maxsum = -1.023
for m in xrange(len(d)):
indc = range(len(d) )
lsnake = [-1]*(len(d)+1)
lsnake[0] = m
lsnake[-1] = m
del indc[m]
temp = d[m].copy()
lsum = 0.0
direction = +1
k = 1
while len(indc) > 1:
maxcit = -111.
for i in xrange(len(indc)):
cuc = ccc(d[indc[i]], temp, mask)
if cuc > maxcit:
maxcit = cuc
qi = indc[i]
lsnake[k] = qi
lsum += maxcit
del indc[indc.index(qi)]
direction = -direction
for i in xrange( 1,len(d) ):
if( direction > 0 ):
if(lsnake[i] == -1):
temp = d[lsnake[i-1]].copy()
#print " forw ",lsnake[i-1]
k = i
break
else:
if(lsnake[len(d) - i] == -1):
temp = d[lsnake[len(d) - i +1]].copy()
#print " back ",lsnake[len(d) - i +1]
k = len(d) - i
break
lsnake[lsnake.index(-1)] = indc[-1]
#print " initial image and lsum ",m,lsum
#print lsnake
if(lsum > maxsum):
maxsum = lsum
init = m
snake = [lsnake[i] for i in xrange(len(d))]
print " Initial image selected : ",init,maxsum
print lsnake
for m in xrange(len(d)): d[snake[m]].write_image(new_stack, m)
else:
# figure the "best" starting image
maxsum = -1.023
for m in xrange(len(d)):
indc = range(len(d) )
lsnake = [m]
del indc[m]
temp = d[m].copy()
lsum = 0.0
while len(indc) > 1:
maxcit = -111.
for i in xrange(len(indc)):
cuc = ccc(d[indc[i]], temp, mask)
if cuc > maxcit:
maxcit = cuc
qi = indc[i]
lsnake.append(qi)
lsum += maxcit
temp = d[qi].copy()
del indc[indc.index(qi)]
lsnake.append(indc[-1])
#print " initial image and lsum ",m,lsum
#print lsnake
if(lsum > maxsum):
maxsum = lsum
init = m
snake = [lsnake[i] for i in xrange(len(d))]
print " Initial image selected : ",init,maxsum
print lsnake
for m in xrange(len(d)): d[snake[m]].write_image(new_stack, m)
def spruce_up_var_m(refdata):
from utilities import print_msg
from utilities import model_circle, get_im
from filter import filt_tanl, filt_gaussl
from morphology import threshold
import os
numref = refdata[0]
outdir = refdata[1]
fscc = refdata[2]
total_iter = refdata[3]
varf = refdata[4]
mask = refdata[5]
ali50S = refdata[6]
if ali50S:
mask_50S = get_im("mask-50S.spi")
if fscc is None:
flmin = 0.4
aamin = 0.1
else:
flmin, aamin, idmin = minfilt(fscc)
aamin = aamin
msg = "Minimum tangent filter: cut-off frequency = %10.3f fall-off = %10.3f\n" % (
fflmin, aamin)
print_msg(msg)
for i in xrange(numref):
volf = get_im(os.path.join(outdir, "vol%04d.hdf" % total_iter), i)
if (not (varf is None)): volf = volf.filter_by_image(varf)
volf = filt_tanl(volf, flmin, aamin)
stat = Util.infomask(volf, mask, True)
volf -= stat[0]
Util.mul_scalar(volf, 1.0 / stat[1])
nx = volf.get_xsize()
stat = Util.infomask(
volf,
model_circle(nx // 2 - 2, nx, nx, nx) -
model_circle(nx // 2 - 6, nx, nx, nx), True)
volf -= stat[0]
Util.mul_img(volf, mask)
volf = threshold(volf)
volf = filt_gaussl(volf, 0.4)
if ali50S:
if i == 0:
v50S_0 = volf.copy()
v50S_0 *= mask_50S
else:
from applications import ali_vol_3
from fundamentals import rot_shift3D
v50S_i = volf.copy()
v50S_i *= mask_50S
params = ali_vol_3(v50S_i, v50S_0, 10.0, 0.5, mask=mask_50S)
volf = rot_shift3D(volf, params[0], params[1], params[2],
params[3], params[4], params[5], 1.0)
volf.write_image(os.path.join(outdir, "volf%04d.hdf" % total_iter), i)
new_stack = args[1]
d = EMData.read_images(stack)
try:
ttt = d[0].get_attr('xform.params2d')
for i in xrange(len(d)):
alpha, sx, sy, mirror, scale = get_params2D(d[i])
d[i] = rot_shift2D(d[i], alpha, sx, sy, mirror)
except:
pass
nx = d[0].get_xsize()
ny = d[0].get_ysize()
if options.radius < 1 : radius = nx//2-2
else: radius = options.radius
mask = model_circle(radius, nx, ny)
init = options.initial
if init > -1 :
print " initial image: %d" % init
temp = d[init].copy()
temp.write_image(new_stack, 0)
del d[init]
k = 1
lsum = 0.0
while len(d) > 1:
maxcit = -111.
for i in xrange(len(d)):
cuc = ccc(d[i], temp, mask)
if cuc > maxcit:
def main():
arglist = []
for arg in sys.argv:
arglist.append(arg)
progname = os.path.basename(arglist[0])
usage = progname + """ firstvolume secondvolume maskfile outputfile --wn --step --cutoff --radius --fsc --res_overall --out_ang_res --apix --MPI
Compute local resolution in real space within area outlined by the maskfile and within regions wn x wn x wn
"""
parser = optparse.OptionParser(usage, version=global_def.SPARXVERSION)
parser.add_option(
"--wn",
type="int",
default=7,
help=
"Size of window within which local real-space FSC is computed. (default 7)"
)
parser.add_option(
"--step",
type="float",
default=1.0,
help="Shell step in Fourier size in pixels. (default 1.0)")
parser.add_option("--cutoff",
type="float",
default=0.5,
help="Resolution cut-off for FSC. (default 0.5)")
parser.add_option(
"--radius",
type="int",
default=-1,
help=
"If there is no maskfile, sphere with r=radius will be used. By default, the radius is nx/2-wn (default -1)"
)
parser.add_option(
"--fsc",
type="string",
default=None,
help=
"Save overall FSC curve (might be truncated). By default, the program does not save the FSC curve. (default none)"
)
parser.add_option(
"--res_overall",
type="float",
default=-1.0,
help=
"Overall resolution at the cutoff level estimated by the user [abs units]. (default None)"
)
parser.add_option(
"--out_ang_res",
action="store_true",
default=False,
help=
"Additionally creates a local resolution file in Angstroms. (default False)"
)
parser.add_option(
"--apix",
type="float",
default=1.0,
help=
"Pixel size in Angstrom. Effective only with --out_ang_res options. (default 1.0)"
)
parser.add_option("--MPI",
action="store_true",
default=False,
help="Use MPI version.")
(options, args) = parser.parse_args(arglist[1:])
if len(args) < 3 or len(args) > 4:
print("See usage " + usage)
sys.exit()
if global_def.CACHE_DISABLE:
utilities.disable_bdb_cache()
res_overall = options.res_overall
if options.MPI:
sys.argv = mpi.mpi_init(len(sys.argv), sys.argv)
number_of_proc = mpi.mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
main_node = 0
global_def.MPI = True
cutoff = options.cutoff
nk = int(options.wn)
if (myid == main_node):
#print sys.argv
vi = utilities.get_im(sys.argv[1])
ui = utilities.get_im(sys.argv[2])
nx = vi.get_xsize()
ny = vi.get_ysize()
nz = vi.get_zsize()
dis = [nx, ny, nz]
else:
dis = [0, 0, 0, 0]
global_def.BATCH = True
dis = utilities.bcast_list_to_all(dis, myid, source_node=main_node)
if (myid != main_node):
nx = int(dis[0])
ny = int(dis[1])
nz = int(dis[2])
vi = utilities.model_blank(nx, ny, nz)
ui = utilities.model_blank(nx, ny, nz)
if len(args) == 3:
m = utilities.model_circle((min(nx, ny, nz) - nk) // 2, nx, ny, nz)
outvol = args[2]
elif len(args) == 4:
if (myid == main_node):
m = morphology.binarize(utilities.get_im(args[2]), 0.5)
else:
m = utilities.model_blank(nx, ny, nz)
outvol = args[3]
utilities.bcast_EMData_to_all(m, myid, main_node)
"""Multiline Comment0"""
freqvol, resolut = statistics.locres(vi, ui, m, nk, cutoff,
options.step, myid, main_node,
number_of_proc)
if (myid == 0):
# Remove outliers based on the Interquartile range
output_volume(freqvol, resolut, options.apix, outvol, options.fsc,
options.out_ang_res, nx, ny, nz, res_overall)
mpi.mpi_finalize()
else:
cutoff = options.cutoff
vi = utilities.get_im(args[0])
ui = utilities.get_im(args[1])
nn = vi.get_xsize()
nk = int(options.wn)
if len(args) == 3:
m = utilities.model_circle((nn - nk) // 2, nn, nn, nn)
outvol = args[2]
elif len(args) == 4:
m = morphology.binarize(utilities.get_im(args[2]), 0.5)
outvol = args[3]
mc = utilities.model_blank(nn, nn, nn, 1.0) - m
vf = fundamentals.fft(vi)
uf = fundamentals.fft(ui)
"""Multiline Comment1"""
lp = int(nn / 2 / options.step + 0.5)
step = 0.5 / lp
freqvol = utilities.model_blank(nn, nn, nn)
resolut = []
for i in range(1, lp):
fl = step * i
fh = fl + step
#print(lp,i,step,fl,fh)
v = fundamentals.fft(filter.filt_tophatb(vf, fl, fh))
u = fundamentals.fft(filter.filt_tophatb(uf, fl, fh))
tmp1 = EMAN2_cppwrap.Util.muln_img(v, v)
tmp2 = EMAN2_cppwrap.Util.muln_img(u, u)
do = EMAN2_cppwrap.Util.infomask(
morphology.square_root(
morphology.threshold(
EMAN2_cppwrap.Util.muln_img(tmp1, tmp2))), m, True)[0]
tmp3 = EMAN2_cppwrap.Util.muln_img(u, v)
dp = EMAN2_cppwrap.Util.infomask(tmp3, m, True)[0]
resolut.append([i, (fl + fh) / 2.0, dp / do])
tmp1 = EMAN2_cppwrap.Util.box_convolution(tmp1, nk)
tmp2 = EMAN2_cppwrap.Util.box_convolution(tmp2, nk)
tmp3 = EMAN2_cppwrap.Util.box_convolution(tmp3, nk)
EMAN2_cppwrap.Util.mul_img(tmp1, tmp2)
tmp1 = morphology.square_root(morphology.threshold(tmp1))
EMAN2_cppwrap.Util.mul_img(tmp1, m)
EMAN2_cppwrap.Util.add_img(tmp1, mc)
EMAN2_cppwrap.Util.mul_img(tmp3, m)
EMAN2_cppwrap.Util.add_img(tmp3, mc)
EMAN2_cppwrap.Util.div_img(tmp3, tmp1)
EMAN2_cppwrap.Util.mul_img(tmp3, m)
freq = (fl + fh) / 2.0
bailout = True
for x in range(nn):
for y in range(nn):
for z in range(nn):
if (m.get_value_at(x, y, z) > 0.5):
if (freqvol.get_value_at(x, y, z) == 0.0):
if (tmp3.get_value_at(x, y, z) < cutoff):
freqvol.set_value_at(x, y, z, freq)
bailout = False
else:
bailout = False
if (bailout): break
#print(len(resolut))
# remove outliers
output_volume(freqvol, resolut, options.apix, outvol, options.fsc,
options.out_ang_res, nx, ny, nz, res_overall)
def ali3d_MPI(stack,
ref_vol,
outdir,
maskfile=None,
ir=1,
ou=-1,
rs=1,
xr="4 2 2 1",
yr="-1",
ts="1 1 0.5 0.25",
delta="10 6 4 4",
an="-1",
center=0,
maxit=5,
term=95,
CTF=False,
fourvar=False,
snr=1.0,
ref_a="S",
sym="c1",
sort=True,
cutoff=999.99,
pix_cutoff="0",
two_tail=False,
model_jump="1 1 1 1 1",
restart=False,
save_half=False,
protos=None,
oplane=None,
lmask=-1,
ilmask=-1,
findseam=False,
vertstep=None,
hpars="-1",
hsearch="0.0 50.0",
full_output=False,
compare_repro=False,
compare_ref_free="-1",
ref_free_cutoff="-1 -1 -1 -1",
wcmask=None,
debug=False,
recon_pad=4,
olmask=75):
from alignment import Numrinit, prepare_refrings
from utilities import model_circle, get_image, drop_image, get_input_from_string
from utilities import bcast_list_to_all, bcast_number_to_all, reduce_EMData_to_root, bcast_EMData_to_all
from utilities import send_attr_dict
from utilities import get_params_proj, file_type
from fundamentals import rot_avg_image
import os
import types
from utilities import print_begin_msg, print_end_msg, print_msg
from mpi import mpi_bcast, mpi_comm_size, mpi_comm_rank, MPI_FLOAT, MPI_COMM_WORLD, mpi_barrier, mpi_reduce
from mpi import mpi_reduce, MPI_INT, MPI_SUM, mpi_finalize
from filter import filt_ctf
from projection import prep_vol, prgs
from statistics import hist_list, varf3d_MPI, fsc_mask
from numpy import array, bincount, array2string, ones
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
if myid == main_node:
if os.path.exists(outdir):
ERROR(
'Output directory exists, please change the name and restart the program',
"ali3d_MPI", 1)
os.mkdir(outdir)
mpi_barrier(MPI_COMM_WORLD)
if debug:
from time import sleep
while not os.path.exists(outdir):
print "Node ", myid, " waiting..."
sleep(5)
info_file = os.path.join(outdir, "progress%04d" % myid)
finfo = open(info_file, 'w')
else:
finfo = None
mjump = get_input_from_string(model_jump)
xrng = get_input_from_string(xr)
if yr == "-1": yrng = xrng
else: yrng = get_input_from_string(yr)
step = get_input_from_string(ts)
delta = get_input_from_string(delta)
ref_free_cutoff = get_input_from_string(ref_free_cutoff)
pix_cutoff = get_input_from_string(pix_cutoff)
lstp = min(len(xrng), len(yrng), len(step), len(delta))
if an == "-1":
an = [-1] * lstp
else:
an = get_input_from_string(an)
# make sure pix_cutoff is set for all iterations
if len(pix_cutoff) < lstp:
for i in xrange(len(pix_cutoff), lstp):
pix_cutoff.append(pix_cutoff[-1])
# don't waste time on sub-pixel alignment for low-resolution ang incr
for i in range(len(step)):
if (delta[i] > 4 or delta[i] == -1) and step[i] < 1:
step[i] = 1
first_ring = int(ir)
rstep = int(rs)
last_ring = int(ou)
max_iter = int(maxit)
center = int(center)
nrefs = EMUtil.get_image_count(ref_vol)
nmasks = 0
if maskfile:
# read number of masks within each maskfile (mc)
nmasks = EMUtil.get_image_count(maskfile)
# open masks within maskfile (mc)
maskF = EMData.read_images(maskfile, xrange(nmasks))
vol = EMData.read_images(ref_vol, xrange(nrefs))
nx = vol[0].get_xsize()
## make sure box sizes are the same
if myid == main_node:
im = EMData.read_images(stack, [0])
bx = im[0].get_xsize()
if bx != nx:
print_msg(
"Error: Stack box size (%i) differs from initial model (%i)\n"
% (bx, nx))
sys.exit()
del im, bx
# for helical processing:
helicalrecon = False
if protos is not None or hpars != "-1" or findseam is True:
helicalrecon = True
# if no out-of-plane param set, use 5 degrees
if oplane is None:
oplane = 5.0
if protos is not None:
proto = get_input_from_string(protos)
if len(proto) != nrefs:
print_msg("Error: insufficient protofilament numbers supplied")
sys.exit()
if hpars != "-1":
hpars = get_input_from_string(hpars)
if len(hpars) != 2 * nrefs:
print_msg("Error: insufficient helical parameters supplied")
sys.exit()
## create helical parameter file for helical reconstruction
if helicalrecon is True and myid == main_node:
from hfunctions import createHpar
# create initial helical parameter files
dp = [0] * nrefs
dphi = [0] * nrefs
vdp = [0] * nrefs
vdphi = [0] * nrefs
for iref in xrange(nrefs):
hpar = os.path.join(outdir, "hpar%02d.spi" % (iref))
params = False
if hpars != "-1":
# if helical parameters explicitly given, set twist & rise
params = [float(hpars[iref * 2]), float(hpars[(iref * 2) + 1])]
dp[iref], dphi[iref], vdp[iref], vdphi[iref] = createHpar(
hpar, proto[iref], params, vertstep)
# get values for helical search parameters
hsearch = get_input_from_string(hsearch)
if len(hsearch) != 2:
print_msg("Error: specify outer and inner radii for helical search")
sys.exit()
if last_ring < 0 or last_ring > int(nx / 2) - 2:
last_ring = int(nx / 2) - 2
if myid == main_node:
# import user_functions
# user_func = user_functions.factory[user_func_name]
print_begin_msg("ali3d_MPI")
print_msg("Input stack : %s\n" % (stack))
print_msg("Reference volume : %s\n" % (ref_vol))
print_msg("Output directory : %s\n" % (outdir))
if nmasks > 0:
print_msg("Maskfile (number of masks) : %s (%i)\n" %
(maskfile, nmasks))
print_msg("Inner radius : %i\n" % (first_ring))
print_msg("Outer radius : %i\n" % (last_ring))
print_msg("Ring step : %i\n" % (rstep))
print_msg("X search range : %s\n" % (xrng))
print_msg("Y search range : %s\n" % (yrng))
print_msg("Translational step : %s\n" % (step))
print_msg("Angular step : %s\n" % (delta))
print_msg("Angular search range : %s\n" % (an))
print_msg("Maximum iteration : %i\n" % (max_iter))
print_msg("Center type : %i\n" % (center))
print_msg("CTF correction : %s\n" % (CTF))
print_msg("Signal-to-Noise Ratio : %f\n" % (snr))
print_msg("Reference projection method : %s\n" % (ref_a))
print_msg("Symmetry group : %s\n" % (sym))
print_msg("Fourier padding for 3D : %i\n" % (recon_pad))
print_msg("Number of reference models : %i\n" % (nrefs))
print_msg("Sort images between models : %s\n" % (sort))
print_msg("Allow images to jump : %s\n" % (mjump))
print_msg("CC cutoff standard dev : %f\n" % (cutoff))
print_msg("Two tail cutoff : %s\n" % (two_tail))
print_msg("Termination pix error : %f\n" % (term))
print_msg("Pixel error cutoff : %s\n" % (pix_cutoff))
print_msg("Restart : %s\n" % (restart))
print_msg("Full output : %s\n" % (full_output))
print_msg("Compare reprojections : %s\n" % (compare_repro))
print_msg("Compare ref free class avgs : %s\n" % (compare_ref_free))
print_msg("Use cutoff from ref free : %s\n" % (ref_free_cutoff))
if protos:
print_msg("Protofilament numbers : %s\n" % (proto))
print_msg("Using helical search range : %s\n" % hsearch)
if findseam is True:
print_msg("Using seam-based reconstruction\n")
if hpars != "-1":
print_msg("Using hpars : %s\n" % hpars)
if vertstep != None:
print_msg("Using vertical step : %.2f\n" % vertstep)
if save_half is True:
print_msg("Saving even/odd halves\n")
for i in xrange(100):
print_msg("*")
print_msg("\n\n")
if maskfile:
if type(maskfile) is types.StringType: mask3D = get_image(maskfile)
else: mask3D = maskfile
else: mask3D = model_circle(last_ring, nx, nx, nx)
numr = Numrinit(first_ring, last_ring, rstep, "F")
mask2D = model_circle(last_ring, nx, nx) - model_circle(first_ring, nx, nx)
fscmask = model_circle(last_ring, nx, nx, nx)
if CTF:
from filter import filt_ctf
from reconstruction_rjh import rec3D_MPI_noCTF
if myid == main_node:
active = EMUtil.get_all_attributes(stack, 'active')
list_of_particles = []
for im in xrange(len(active)):
if active[im]: list_of_particles.append(im)
del active
nima = len(list_of_particles)
else:
nima = 0
total_nima = bcast_number_to_all(nima, source_node=main_node)
if myid != main_node:
list_of_particles = [-1] * total_nima
list_of_particles = bcast_list_to_all(list_of_particles,
source_node=main_node)
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
# create a list of images for each node
list_of_particles = list_of_particles[image_start:image_end]
nima = len(list_of_particles)
if debug:
finfo.write("image_start, image_end: %d %d\n" %
(image_start, image_end))
finfo.flush()
data = EMData.read_images(stack, list_of_particles)
t_zero = Transform({
"type": "spider",
"phi": 0,
"theta": 0,
"psi": 0,
"tx": 0,
"ty": 0
})
transmulti = [[t_zero for i in xrange(nrefs)] for j in xrange(nima)]
for iref, im in ((iref, im) for iref in xrange(nrefs)
for im in xrange(nima)):
if nrefs == 1:
transmulti[im][iref] = data[im].get_attr("xform.projection")
else:
# if multi models, keep track of eulers for all models
try:
transmulti[im][iref] = data[im].get_attr("eulers_txty.%i" %
iref)
except:
data[im].set_attr("eulers_txty.%i" % iref, t_zero)
scoremulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
pixelmulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
ref_res = [0.0 for x in xrange(nrefs)]
apix = data[0].get_attr('apix_x')
# for oplane parameter, create cylindrical mask
if oplane is not None and myid == main_node:
from hfunctions import createCylMask
cmaskf = os.path.join(outdir, "mask3D_cyl.mrc")
mask3D = createCylMask(data, olmask, lmask, ilmask, cmaskf)
# if finding seam of helix, create wedge masks
if findseam is True:
wedgemask = []
for pf in xrange(nrefs):
wedgemask.append(EMData())
# wedgemask option
if wcmask is not None:
wcmask = get_input_from_string(wcmask)
if len(wcmask) != 3:
print_msg(
"Error: wcmask option requires 3 values: x y radius")
sys.exit()
# determine if particles have helix info:
try:
data[0].get_attr('h_angle')
original_data = []
boxmask = True
from hfunctions import createBoxMask
except:
boxmask = False
# prepare particles
for im in xrange(nima):
data[im].set_attr('ID', list_of_particles[im])
data[im].set_attr('pix_score', int(0))
if CTF:
# only phaseflip particles, not full CTF correction
ctf_params = data[im].get_attr("ctf")
st = Util.infomask(data[im], mask2D, False)
data[im] -= st[0]
data[im] = filt_ctf(data[im], ctf_params, sign=-1, binary=1)
data[im].set_attr('ctf_applied', 1)
# for window mask:
if boxmask is True:
h_angle = data[im].get_attr("h_angle")
original_data.append(data[im].copy())
bmask = createBoxMask(nx, apix, ou, lmask, h_angle)
data[im] *= bmask
del bmask
if debug:
finfo.write('%d loaded \n' % nima)
finfo.flush()
if myid == main_node:
# initialize data for the reference preparation function
ref_data = [mask3D, max(center, 0), None, None, None, None]
# for method -1, switch off centering in user function
from time import time
# this is needed for gathering of pixel errors
disps = []
recvcount = []
disps_score = []
recvcount_score = []
for im in xrange(number_of_proc):
if (im == main_node):
disps.append(0)
disps_score.append(0)
else:
disps.append(disps[im - 1] + recvcount[im - 1])
disps_score.append(disps_score[im - 1] + recvcount_score[im - 1])
ib, ie = MPI_start_end(total_nima, number_of_proc, im)
recvcount.append(ie - ib)
recvcount_score.append((ie - ib) * nrefs)
pixer = [0.0] * nima
cs = [0.0] * 3
total_iter = 0
volodd = EMData.read_images(ref_vol, xrange(nrefs))
voleve = EMData.read_images(ref_vol, xrange(nrefs))
if restart:
# recreate initial volumes from alignments stored in header
itout = "000_00"
for iref in xrange(nrefs):
if (nrefs == 1):
modout = ""
else:
modout = "_model_%02d" % (iref)
if (sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',
transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection', transmulti[im][iref])
fscfile = os.path.join(outdir, "fsc_%s%s" % (itout, modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(
data,
sym,
fscmask,
fscfile,
myid,
main_node,
index=group,
npad=recon_pad)
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep = None
if vertstep is not None:
vstep = (vdp[iref], vdphi[iref])
print_msg(
"Old rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
hvals = processHelicalVol(vol[iref], voleve[iref],
volodd[iref], iref, outdir,
itout, dp[iref], dphi[iref],
apix, hsearch, findseam, vstep,
wcmask)
(vol[iref], voleve[iref], volodd[iref], dp[iref],
dphi[iref], vdp[iref], vdphi[iref]) = hvals
print_msg(
"New rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask(volodd[iref], voleve[iref], mask3D, rstep,
fscfile)
else:
vol[iref].write_image(
os.path.join(outdir, "vol_%s.hdf" % itout), -1)
if save_half is True:
volodd[iref].write_image(
os.path.join(outdir, "volodd_%s.hdf" % itout), -1)
voleve[iref].write_image(
os.path.join(outdir, "voleve_%s.hdf" % itout), -1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs, fl = ref_ali3d(ref_data)
vol[iref].write_image(
os.path.join(outdir, "volf_%s.hdf" % (itout)), -1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res = fl
else:
res_msg = "Models filtered at resolution of: \t"
res = apix / fl
ares = array2string(array(res), precision=2)
print_msg("%s%s\n\n" % (res_msg, ares))
bcast_EMData_to_all(vol[iref], myid, main_node)
# write out headers, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
# projection matching
for N_step in xrange(lstp):
terminate = 0
Iter = -1
while (Iter < max_iter - 1 and terminate == 0):
Iter += 1
total_iter += 1
itout = "%03g_%02d" % (delta[N_step], Iter)
if myid == main_node:
print_msg(
"ITERATION #%3d, inner iteration #%3d\nDelta = %4.1f, an = %5.2f, xrange = %5.2f, yrange = %5.2f, step = %5.2f\n\n"
% (N_step, Iter, delta[N_step], an[N_step], xrng[N_step],
yrng[N_step], step[N_step]))
for iref in xrange(nrefs):
if myid == main_node: start_time = time()
volft, kb = prep_vol(vol[iref])
## constrain projections to out of plane parameter
theta1 = None
theta2 = None
if oplane is not None:
theta1 = 90 - oplane
theta2 = 90 + oplane
refrings = prepare_refrings(volft,
kb,
nx,
delta[N_step],
ref_a,
sym,
numr,
MPI=True,
phiEqpsi="Minus",
initial_theta=theta1,
delta_theta=theta2)
del volft, kb
if myid == main_node:
print_msg(
"Time to prepare projections for model %i: %s\n" %
(iref, legibleTime(time() - start_time)))
start_time = time()
for im in xrange(nima):
data[im].set_attr("xform.projection", transmulti[im][iref])
if an[N_step] == -1:
t1, peak, pixer[im] = proj_ali_incore(
data[im], refrings, numr, xrng[N_step],
yrng[N_step], step[N_step], finfo)
else:
t1, peak, pixer[im] = proj_ali_incore_local(
data[im], refrings, numr, xrng[N_step],
yrng[N_step], step[N_step], an[N_step], finfo)
#data[im].set_attr("xform.projection"%iref, t1)
if nrefs > 1:
data[im].set_attr("eulers_txty.%i" % iref, t1)
scoremulti[im][iref] = peak
from pixel_error import max_3D_pixel_error
# t1 is the current param, t2 is old
t2 = transmulti[im][iref]
pixelmulti[im][iref] = max_3D_pixel_error(t1, t2, numr[-3])
transmulti[im][iref] = t1
if myid == main_node:
print_msg("Time of alignment for model %i: %s\n" %
(iref, legibleTime(time() - start_time)))
start_time = time()
# gather scoring data from all processors
from mpi import mpi_gatherv
scoremultisend = sum(scoremulti, [])
pixelmultisend = sum(pixelmulti, [])
tmp = mpi_gatherv(scoremultisend, len(scoremultisend), MPI_FLOAT,
recvcount_score, disps_score, MPI_FLOAT,
main_node, MPI_COMM_WORLD)
tmp1 = mpi_gatherv(pixelmultisend, len(pixelmultisend), MPI_FLOAT,
recvcount_score, disps_score, MPI_FLOAT,
main_node, MPI_COMM_WORLD)
tmp = mpi_bcast(tmp, (total_nima * nrefs), MPI_FLOAT, 0,
MPI_COMM_WORLD)
tmp1 = mpi_bcast(tmp1, (total_nima * nrefs), MPI_FLOAT, 0,
MPI_COMM_WORLD)
tmp = map(float, tmp)
tmp1 = map(float, tmp1)
score = array(tmp).reshape(-1, nrefs)
pixelerror = array(tmp1).reshape(-1, nrefs)
score_local = array(scoremulti)
mean_score = score.mean(axis=0)
std_score = score.std(axis=0)
cut = mean_score - (cutoff * std_score)
cut2 = mean_score + (cutoff * std_score)
res_max = score_local.argmax(axis=1)
minus_cc = [0.0 for x in xrange(nrefs)]
minus_pix = [0.0 for x in xrange(nrefs)]
minus_ref = [0.0 for x in xrange(nrefs)]
#output pixel errors
if (myid == main_node):
from statistics import hist_list
lhist = 20
pixmin = pixelerror.min(axis=1)
region, histo = hist_list(pixmin, lhist)
if (region[0] < 0.0): region[0] = 0.0
print_msg(
"Histogram of pixel errors\n ERROR number of particles\n"
)
for lhx in xrange(lhist):
print_msg(" %10.3f %7d\n" % (region[lhx], histo[lhx]))
# Terminate if 95% within 1 pixel error
im = 0
for lhx in xrange(lhist):
if (region[lhx] > 1.0): break
im += histo[lhx]
print_msg("Percent of particles with pixel error < 1: %f\n\n" %
(im / float(total_nima) * 100))
term_cond = float(term) / 100
if (im / float(total_nima) > term_cond):
terminate = 1
print_msg("Terminating internal loop\n")
del region, histo
terminate = mpi_bcast(terminate, 1, MPI_INT, 0, MPI_COMM_WORLD)
terminate = int(terminate[0])
for im in xrange(nima):
if (sort == False):
data[im].set_attr('group', 999)
elif (mjump[N_step] == 1):
data[im].set_attr('group', int(res_max[im]))
pix_run = data[im].get_attr('pix_score')
if (pix_cutoff[N_step] == 1
and (terminate == 1 or Iter == max_iter - 1)):
if (pixelmulti[im][int(res_max[im])] > 1):
data[im].set_attr('pix_score', int(777))
if (score_local[im][int(res_max[im])] < cut[int(
res_max[im])]) or (two_tail and score_local[im][int(
res_max[im])] > cut2[int(res_max[im])]):
data[im].set_attr('group', int(888))
minus_cc[int(res_max[im])] = minus_cc[int(res_max[im])] + 1
if (pix_run == 777):
data[im].set_attr('group', int(777))
minus_pix[int(
res_max[im])] = minus_pix[int(res_max[im])] + 1
if (compare_ref_free != "-1") and (ref_free_cutoff[N_step] !=
-1) and (total_iter > 1):
id = data[im].get_attr('ID')
if id in rejects:
data[im].set_attr('group', int(666))
minus_ref[int(
res_max[im])] = minus_ref[int(res_max[im])] + 1
minus_cc_tot = mpi_reduce(minus_cc, nrefs, MPI_FLOAT, MPI_SUM, 0,
MPI_COMM_WORLD)
minus_pix_tot = mpi_reduce(minus_pix, nrefs, MPI_FLOAT, MPI_SUM, 0,
MPI_COMM_WORLD)
minus_ref_tot = mpi_reduce(minus_ref, nrefs, MPI_FLOAT, MPI_SUM, 0,
MPI_COMM_WORLD)
if (myid == main_node):
if (sort):
tot_max = score.argmax(axis=1)
res = bincount(tot_max)
else:
res = ones(nrefs) * total_nima
print_msg("Particle distribution: \t\t%s\n" % (res * 1.0))
afcut1 = res - minus_cc_tot
afcut2 = afcut1 - minus_pix_tot
afcut3 = afcut2 - minus_ref_tot
print_msg("Particle distribution after cc cutoff:\t\t%s\n" %
(afcut1))
print_msg("Particle distribution after pix cutoff:\t\t%s\n" %
(afcut2))
print_msg("Particle distribution after ref cutoff:\t\t%s\n\n" %
(afcut3))
res = [0.0 for i in xrange(nrefs)]
for iref in xrange(nrefs):
if (center == -1):
from utilities import estimate_3D_center_MPI, rotate_3D_shift
dummy = EMData()
cs[0], cs[1], cs[2], dummy, dummy = estimate_3D_center_MPI(
data, total_nima, myid, number_of_proc, main_node)
cs = mpi_bcast(cs, 3, MPI_FLOAT, main_node, MPI_COMM_WORLD)
cs = [-float(cs[0]), -float(cs[1]), -float(cs[2])]
rotate_3D_shift(data, cs)
if (sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',
transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection',
transmulti[im][iref])
if (nrefs == 1):
modout = ""
else:
modout = "_model_%02d" % (iref)
fscfile = os.path.join(outdir, "fsc_%s%s" % (itout, modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(
data,
sym,
fscmask,
fscfile,
myid,
main_node,
index=group,
npad=recon_pad)
if myid == main_node:
print_msg("3D reconstruction time for model %i: %s\n" %
(iref, legibleTime(time() - start_time)))
start_time = time()
# Compute Fourier variance
if fourvar:
outvar = os.path.join(outdir, "volVar_%s.hdf" % (itout))
ssnr_file = os.path.join(outdir, "ssnr_%s" % (itout))
varf = varf3d_MPI(data,
ssnr_text_file=ssnr_file,
mask2D=None,
reference_structure=vol[iref],
ou=last_ring,
rw=1.0,
npad=1,
CTF=None,
sign=1,
sym=sym,
myid=myid)
if myid == main_node:
print_msg(
"Time to calculate 3D Fourier variance for model %i: %s\n"
% (iref, legibleTime(time() - start_time)))
start_time = time()
varf = 1.0 / varf
varf.write_image(outvar, -1)
else:
varf = None
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep = None
if vertstep is not None:
vstep = (vdp[iref], vdphi[iref])
print_msg(
"Old rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
hvals = processHelicalVol(vol[iref], voleve[iref],
volodd[iref], iref, outdir,
itout, dp[iref], dphi[iref],
apix, hsearch, findseam,
vstep, wcmask)
(vol[iref], voleve[iref], volodd[iref], dp[iref],
dphi[iref], vdp[iref], vdphi[iref]) = hvals
print_msg(
"New rise and twist for model %i : %8.3f, %8.3f\n"
% (iref, dp[iref], dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask(volodd[iref], voleve[iref], mask3D,
rstep, fscfile)
print_msg(
"Time to search and apply helical symmetry for model %i: %s\n\n"
% (iref, legibleTime(time() - start_time)))
start_time = time()
else:
vol[iref].write_image(
os.path.join(outdir, "vol_%s.hdf" % (itout)), -1)
if save_half is True:
volodd[iref].write_image(
os.path.join(outdir, "volodd_%s.hdf" % (itout)),
-1)
voleve[iref].write_image(
os.path.join(outdir, "voleve_%s.hdf" % (itout)),
-1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
ref_data[4] = varf
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs, fl = ref_ali3d(ref_data)
vol[iref].write_image(
os.path.join(outdir, "volf_%s.hdf" % (itout)), -1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res[iref] = fl
else:
res_msg = "Models filtered at resolution of: \t"
res[iref] = apix / fl
del varf
bcast_EMData_to_all(vol[iref], myid, main_node)
if compare_ref_free != "-1": compare_repro = True
if compare_repro:
outfile_repro = comp_rep(refrings, data, itout, modout,
vol[iref], group, nima, nx, myid,
main_node, outdir)
mpi_barrier(MPI_COMM_WORLD)
if compare_ref_free != "-1":
ref_free_output = os.path.join(
outdir, "ref_free_%s%s" % (itout, modout))
rejects = compare(compare_ref_free, outfile_repro,
ref_free_output, yrng[N_step],
xrng[N_step], rstep, nx, apix,
ref_free_cutoff[N_step],
number_of_proc, myid, main_node)
# retrieve alignment params from all processors
par_str = ['xform.projection', 'ID', 'group']
if nrefs > 1:
for iref in xrange(nrefs):
par_str.append('eulers_txty.%i' % iref)
if myid == main_node:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start,
image_end, number_of_proc)
else:
send_attr_dict(main_node, data, par_str, image_start,
image_end)
if myid == main_node:
ares = array2string(array(res), precision=2)
print_msg("%s%s\n\n" % (res_msg, ares))
dummy = EMData()
if full_output:
nimat = EMUtil.get_image_count(stack)
output_file = os.path.join(outdir, "paramout_%s" % itout)
foutput = open(output_file, 'w')
for im in xrange(nimat):
# save the parameters for each of the models
outstring = ""
dummy.read_image(stack, im, True)
param3d = dummy.get_attr('xform.projection')
g = dummy.get_attr("group")
# retrieve alignments in EMAN-format
pE = param3d.get_params('eman')
outstring += "%f\t%f\t%f\t%f\t%f\t%i\n" % (
pE["az"], pE["alt"], pE["phi"], pE["tx"], pE["ty"],
g)
foutput.write(outstring)
foutput.close()
del dummy
mpi_barrier(MPI_COMM_WORLD)
# mpi_finalize()
if myid == main_node: print_end_msg("ali3d_MPI")
def run3Dalignment(paramsdict, partids, partstack, outputdir, procid, myid, main_node, nproc):
# Reads from paramsdict["stack"] particles partids set parameters in partstack
# and do refinement as specified in paramsdict
#
# Will create outputdir
# Will write to outputdir output parameters: params-chunk0.txt and params-chunk1.txt
if(myid == main_node):
# Create output directory
log = Logger(BaseLogger_Files())
log.prefix = os.path.join(outputdir)
#cmd = "mkdir "+log.prefix
#junk = cmdexecute(cmd)
log.prefix += "/"
else: log = None
mpi_barrier(MPI_COMM_WORLD)
ali3d_options.delta = paramsdict["delta"]
ali3d_options.ts = paramsdict["ts"]
ali3d_options.xr = paramsdict["xr"]
# low pass filter is applied to shrank data, so it has to be adjusted
ali3d_options.fl = paramsdict["lowpass"]/paramsdict["shrink"]
ali3d_options.initfl = paramsdict["initialfl"]/paramsdict["shrink"]
ali3d_options.aa = paramsdict["falloff"]
ali3d_options.maxit = paramsdict["maxit"]
ali3d_options.mask3D = paramsdict["mask3D"]
ali3d_options.an = paramsdict["an"]
ali3d_options.ou = paramsdict["radius"] # This is changed in ali3d_base, but the shrank value is needed in vol recons, fixt it!
shrinkage = paramsdict["shrink"]
projdata = getindexdata(paramsdict["stack"], partids, partstack, myid, nproc)
onx = projdata[0].get_xsize()
last_ring = ali3d_options.ou
if last_ring < 0: last_ring = int(onx/2) - 2
mask2D = model_circle(last_ring,onx,onx) - model_circle(ali3d_options.ir,onx,onx)
if(shrinkage < 1.0):
# get the new size
masks2D = resample(mask2D, shrinkage)
nx = masks2D.get_xsize()
masks2D = model_circle(int(last_ring*shrinkage+0.5),nx,nx) - model_circle(max(int(ali3d_options.ir*shrinkage+0.5),1),nx,nx)
nima = len(projdata)
oldshifts = [0.0,0.0]*nima
for im in xrange(nima):
#data[im].set_attr('ID', list_of_particles[im])
ctf_applied = projdata[im].get_attr_default('ctf_applied', 0)
phi,theta,psi,sx,sy = get_params_proj(projdata[im])
projdata[im] = fshift(projdata[im], sx, sy)
set_params_proj(projdata[im],[phi,theta,psi,0.0,0.0])
# For local SHC set anchor
#if(nsoft == 1 and an[0] > -1):
# set_params_proj(data[im],[phi,tetha,psi,0.0,0.0], "xform.anchor")
oldshifts[im] = [sx,sy]
if ali3d_options.CTF :
ctf_params = projdata[im].get_attr("ctf")
if ctf_applied == 0:
st = Util.infomask(projdata[im], mask2D, False)
projdata[im] -= st[0]
projdata[im] = filt_ctf(projdata[im], ctf_params)
projdata[im].set_attr('ctf_applied', 1)
if(shrinkage < 1.0):
#phi,theta,psi,sx,sy = get_params_proj(projdata[im])
projdata[im] = resample(projdata[im], shrinkage)
st = Util.infomask(projdata[im], None, True)
projdata[im] -= st[0]
st = Util.infomask(projdata[im], masks2D, True)
projdata[im] /= st[1]
#sx *= shrinkage
#sy *= shrinkage
#set_params_proj(projdata[im], [phi,theta,psi,sx,sy])
if ali3d_options.CTF :
ctf_params.apix /= shrinkage
projdata[im].set_attr('ctf', ctf_params)
else:
st = Util.infomask(projdata[im], None, True)
projdata[im] -= st[0]
st = Util.infomask(projdata[im], mask2D, True)
projdata[im] /= st[1]
del mask2D
if(shrinkage < 1.0): del masks2D
"""
if(paramsdict["delpreviousmax"]):
for i in xrange(len(projdata)):
try: projdata[i].del_attr("previousmax")
except: pass
"""
if(myid == main_node):
print_dict(paramsdict,"3D alignment parameters")
print(" => actual lowpass : "******" => actual init lowpass : "******" => PW adjustment : ",ali3d_options.pwreference)
print(" => partids : ",partids)
print(" => partstack : ",partstack)
if(ali3d_options.fl > 0.46): ERROR("Low pass filter in 3D alignment > 0.46 on the scale of shrank data","sxcenter_projections",1,myid)
# Run alignment command, it returns params per CPU
params = center_projections_3D(projdata, paramsdict["refvol"], \
ali3d_options, onx, shrinkage, \
mpi_comm = MPI_COMM_WORLD, myid = myid, main_node = main_node, log = log )
del log, projdata
params = wrap_mpi_gatherv(params, main_node, MPI_COMM_WORLD)
# store params
if(myid == main_node):
for im in xrange(nima):
params[im][0] = params[im][0]/shrinkage +oldshifts[im][0]
params[im][1] = params[im][1]/shrinkage +oldshifts[im][1]
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Executed successfully: ","3D alignment"," number of images:%7d"%len(params))
write_text_row(params, os.path.join(outputdir,"params.txt") )
def main():
from utilities import write_text_row, drop_image, model_gauss_noise, get_im, set_params_proj, wrap_mpi_bcast, model_circle
import user_functions
from applications import MPI_start_end
from optparse import OptionParser
from global_def import SPARXVERSION
from EMAN2 import EMData
from multi_shc import multi_shc, do_volume
from logger import Logger, BaseLogger_Files
import sys
import os
import time
import socket
progname = os.path.basename(sys.argv[0])
usage = progname + " stack [output_directory] initial_volume --ir=inner_radius --ou=outer_radius --rs=ring_step --xr=x_range --yr=y_range --ts=translational_search_step --delta=angular_step --an=angular_neighborhood --center=center_type --fl --aa --ref_a=S --sym=c1"
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--ir", type= "int", default= 1, help="inner radius for rotational correlation > 0 (set to 1)")
parser.add_option("--ou", type= "int", default= -1, help="outer radius for rotational correlation < int(nx/2)-1 (set to the radius of the particle)")
parser.add_option("--rs", type= "int", default= 1, help="step between rings in rotational correlation >0 (set to 1)" )
parser.add_option("--xr", type="string", default= "-1", help="range for translation search in x direction, search is +/xr (default 0)")
parser.add_option("--yr", type="string", default= "-1", help="range for translation search in y direction, search is +/yr (default = same as xr)")
parser.add_option("--ts", type="string", default= "1", help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional")
parser.add_option("--delta", type="string", default= "-1", help="angular step of reference projections during initialization step (default automatically selected based on radius of the structure.)")
#parser.add_option("--an", type="string", default= "-1", help="angular neighborhood for local searches (phi and theta)")
parser.add_option("--center", type="float", default= -1, help="-1: average shift method; 0: no centering; 1: center of gravity (default=-1)")
parser.add_option("--maxit", type="int", default= 400, help="maximum number of iterations performed for the GA part (set to 400) ")
parser.add_option("--outlier_percentile",type="float", default= 95, help="percentile above which outliers are removed every iteration")
parser.add_option("--iteration_start",type="int", default= 0, help="starting iteration for rviper, 0 means go to the most recent one (default).")
parser.add_option("--CTF", action="store_true", default=False, help="Use CTF (Default no CTF correction)")
parser.add_option("--snr", type="float", default= 1.0, help="Signal-to-Noise Ratio of the data (default 1.0)")
parser.add_option("--ref_a", type="string", default= "S", help="method for generating the quasi-uniformly distributed projection directions (default S)")
parser.add_option("--sym", type="string", default= "c1", help="symmetry of the refined structure")
parser.add_option("--npad", type="int", default= 2, help="padding size for 3D reconstruction (default=2)")
parser.add_option("--startangles", action="store_true", default=False, help="Use orientation parameters in the input file header to jumpstart the procedure")
#options introduced for the do_volume function
parser.add_option("--fl", type="float", default=0.12, help="cut-off frequency of hyperbolic tangent low-pass Fourier filte (default 0.12)")
parser.add_option("--aa", type="float", default=0.1, help="fall-off of hyperbolic tangent low-pass Fourier filter (default 0.1)")
parser.add_option("--pwreference", type="string", default="", help="text file with a reference power spectrum (default no power spectrum adjustment)")
parser.add_option("--mask3D", type="string", default=None, help="3D mask file (default a sphere WHAT RADIUS??)")
(options, args) = parser.parse_args(sys.argv[1:])
#print( " args ",args)
if( len(args) == 3):
volinit = args[2]
masterdir = args[1]
elif(len(args) == 2):
volinit = args[1]
masterdir = ""
else:
print( "usage: " + usage)
print( "Please run '" + progname + " -h' for detailed options")
return 1
orgstack = args[0]
#print( orgstack,masterdir,volinit )
# INPUT PARAMETERS
radi = options.ou
global_def.BATCH = True
ali3d_options.ir = options.ir
ali3d_options.rs = options.rs
ali3d_options.ou = options.ou
ali3d_options.xr = options.xr
ali3d_options.yr = options.yr
ali3d_options.ts = options.ts
ali3d_options.an = "-1"
ali3d_options.sym = options.sym
ali3d_options.delta = options.delta
ali3d_options.npad = options.npad
ali3d_options.center = options.center
ali3d_options.CTF = options.CTF
ali3d_options.ref_a = options.ref_a
ali3d_options.snr = options.snr
ali3d_options.mask3D = options.mask3D
ali3d_options.pwreference = options.pwreference
ali3d_options.fl = 0.4
ali3d_options.aa = 0.1
if( ali3d_options.xr == "-1" ): ali3d_options.xr = "2"
"""
print( options)
print( 'ali3d_options', ali3d_options.ir ,\
ali3d_options.rs ,\
ali3d_options.ou ,\
ali3d_options.xr ,\
ali3d_options.yr ,\
ali3d_options.ts ,\
ali3d_options.an ,\
ali3d_options.sym ,\
ali3d_options.delta ,\
ali3d_options.npad ,\
ali3d_options.center ,\
ali3d_options.CTF ,\
ali3d_options.ref_a ,\
ali3d_options.snr ,\
ali3d_options.mask3D ,\
ali3d_options.fl ,\
ali3d_options.aa \
)
#exit()
"""
mpi_init(0, [])
nproc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
#mpi_finalize()
#exit()
nxinit = -1 #int(280*0.3*2)
nsoft = 0
mempernode = 4.0e9
# PARAMETERS OF THE PROCEDURE
# threshold error
thresherr = 0
fq = 0.11 # low-freq limit to which fuse ref volumes. Should it be estimated somehow?
# Get the pixel size, if none set to 1.0, and the original image size
if(myid == main_node):
a = get_im(orgstack)
nnxo = a.get_xsize()
if ali3d_options.CTF:
i = a.get_attr('ctf')
pixel_size = i.apix
else:
pixel_size = 1.0
del a
else:
nnxo = 0
pixel_size = 1.0
pixel_size = bcast_number_to_all(pixel_size, source_node = main_node)
nnxo = bcast_number_to_all(nnxo, source_node = main_node)
if(radi < 1): radi = nnxo//2-2
elif((2*radi+2)>nnxo): ERROR("HERE","particle radius set too large!",1)
ali3d_options.ou = radi
if(nxinit < 0): nxinit = min(32, nnxo)
nxshrink = nxinit
minshrink = 32.0/float(nnxo)
shrink = max(float(nxshrink)/float(nnxo),minshrink)
# MASTER DIRECTORY
if(myid == main_node):
print( " masterdir ",masterdir)
if( masterdir == ""):
timestring = strftime("_%d_%b_%Y_%H_%M_%S", localtime())
masterdir = "master"+timestring
li = len(masterdir)
cmd = "{} {}".format("mkdir", masterdir)
cmdexecute(cmd)
keepchecking = 0
else:
li = 0
keepchecking = 1
else:
li = 0
keepchecking = 1
li = mpi_bcast(li,1,MPI_INT,main_node,MPI_COMM_WORLD)[0]
if( li > 0 ):
masterdir = mpi_bcast(masterdir,li,MPI_CHAR,main_node,MPI_COMM_WORLD)
masterdir = string.join(masterdir,"")
# create a vstack from input stack to the local stack in masterdir
# Stack name set to default
stack = "bdb:"+masterdir+"/rdata"
# Initialization of stacks
if(myid == main_node):
if keepchecking:
if(os.path.exists(os.path.join(masterdir,"EMAN2DB/rdata.bdb"))): doit = False
else: doit = True
else: doit = True
if doit:
if(orgstack[:4] == "bdb:"): cmd = "{} {} {}".format("e2bdb.py", orgstack,"--makevstack="+stack)
else: cmd = "{} {} {}".format("sxcpy.py", orgstack, stack)
cmdexecute(cmd)
cmd = "{} {}".format("sxheader.py --consecutive --params=originalid", stack)
cmdexecute(cmd)
keepchecking = False
total_stack = EMUtil.get_image_count(stack)
junk = get_im(stack)
nnxo = junk.get_xsize()
del junk
else:
total_stack = 0
nnxo = 0
total_stack = bcast_number_to_all(total_stack, source_node = main_node)
nnxo = bcast_number_to_all(nnxo, source_node = main_node)
# INITIALIZATION
# Run exhaustive projection matching to get initial orientation parameters
# Estimate initial resolution
initdir = os.path.join(masterdir,"main000")
# make sure the initial volume is not set to zero outside of a mask, as if it is it will crach the program
if( myid == main_node and (not options.startangles)):
viv = get_im(volinit)
if(options.mask3D == None): mask33d = model_circle(radi,nnxo,nnxo,nnxo)
else: mask33d = (options.mask3D).copy()
st = Util.infomask(viv, mask33d, False)
if( st[0] == 0.0 ):
viv += (model_blank(nnxo,nnxo,nnxo,1.0) - mask33d)*model_gauss_noise(st[1]/1000.0,nnxo,nnxo,nnxo)
viv.write_image(volinit)
del mask33d, viv
doit, keepchecking = checkstep(initdir, keepchecking, myid, main_node)
if doit:
partids = os.path.join(masterdir, "ids.txt")
partstack = os.path.join(masterdir, "paramszero.txt")
xr = min(8,(nnxo - (2*radi+1))//2)
if(xr > 3): ts = "2"
else: ts = "1"
delta = int(options.delta)
if(delta <= 0.0):
delta = "%f"%round(degrees(atan(1.0/float(radi))), 2)
paramsdict = { "stack":stack,"delta":"2.0", "ts":ts, "xr":"%f"%xr, "an":"-1", "center":options.center, "maxit":1, \
"currentres":0.4, "aa":0.1, "radius":radi, "nsoft":0, "delpreviousmax":True, "shrink":1.0, "saturatecrit":1.0, \
"refvol":volinit, "mask3D":options.mask3D}
if(options.startangles):
if( myid == main_node ):
cmd = "mkdir "+initdir
cmdexecute(cmd)
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"INITIALIZATION")
cmd = "{} {}".format("sxheader.py --params=xform.projection --export="+os.path.join(initdir,"params-chunk0.txt"), stack)
cmdexecute(cmd)
print(line,"Executed successfully: ","Imported initial parameters from the input stack")
else:
if( myid == main_node ):
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"INITIALIZATION")
write_text_file(range(total_stack), partids)
write_text_row([[0.0,0.0,0.0,0.0,0.0] for i in xrange(total_stack) ], partstack)
metamove(paramsdict, partids, partstack, initdir, 0, myid, main_node, nproc)
if(myid == main_node):
print(line,"Executed successfully: ","initialization ali3d_base_MPI %d"%nsoft)
# store params
partids = [None]*2
for procid in xrange(2): partids[procid] = os.path.join(initdir,"chunk%01d.txt"%procid)
partstack = [None]*2
for procid in xrange(2): partstack[procid] = os.path.join(initdir,"params-chunk%01d.txt"%procid)
from random import shuffle
if(myid == main_node):
# split randomly
params = read_text_row(os.path.join(initdir,"params-chunk0.txt"))
assert(len(params) == total_stack)
ll = range(total_stack)
shuffle(ll)
l1 = ll[:total_stack//2]
l2 = ll[total_stack//2:]
del ll
l1.sort()
l2.sort()
write_text_file(l1,partids[0])
write_text_file(l2,partids[1])
write_text_row([params[i] for i in l1], partstack[0])
write_text_row([params[i] for i in l2], partstack[1])
del params, l1, l2
mpi_barrier(MPI_COMM_WORLD)
# Now parallel
vol = [None]*2
for procid in xrange(2):
projdata = getindexdata(stack, partids[procid], partstack[procid], myid, nproc)
if ali3d_options.CTF: vol[procid] = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol[procid] = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if( myid == main_node):
vol[procid].write_image(os.path.join(initdir,"vol%01d.hdf"%procid) )
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print( line,"Generated inivol #%01d "%procid)
if(myid == main_node):
currentres = get_resolution(vol, radi, nnxo, initdir)
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print( line,"Initial resolution %6.4f"%currentres)
write_text_file([currentres],os.path.join(initdir,"current_resolution.txt"))
else: currentres = 0.0
currentres = bcast_number_to_all(currentres, source_node = main_node)
else:
if(myid == main_node): currentres = read_text_file(os.path.join(initdir,"current_resolution.txt"))[0]
else: currentres = 0.0
currentres = bcast_number_to_all(currentres, source_node = main_node)
# set for the first iteration
nxshrink = min(max(32, int((currentres+paramsdict["aa"]/2.)*2*nnxo + 0.5)), nnxo)
shrink = float(nxshrink)/nnxo
tracker = {"previous-resolution":currentres, "movedup":False,"eliminated-outliers":False,\
"previous-nx":nxshrink, "previous-shrink":shrink, "extension":0, "bestsolution":0}
previousoutputdir = initdir
# MAIN ITERATION
mainiteration = 0
keepgoing = 1
while(keepgoing):
mainiteration += 1
# prepare output directory
mainoutputdir = os.path.join(masterdir,"main%03d"%mainiteration)
if(myid == main_node):
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"MAIN ITERATION #",mainiteration, shrink, nxshrink)
if keepchecking:
if(os.path.exists(mainoutputdir)):
doit = 0
print("Directory ",mainoutputdir," exists!")
else:
doit = 1
keepchecking = False
else:
doit = 1
if doit:
cmd = "{} {}".format("mkdir", mainoutputdir)
cmdexecute(cmd)
# prepare names of input file names, they are in main directory,
# log subdirectories contain outputs from specific refinements
partids = [None]*2
for procid in xrange(2): partids[procid] = os.path.join(previousoutputdir,"chunk%01d.txt"%procid)
partstack = [None]*2
for procid in xrange(2): partstack[procid] = os.path.join(previousoutputdir,"params-chunk%01d.txt"%procid)
mpi_barrier(MPI_COMM_WORLD)
#mpi_finalize()
#exit()
#print("RACING A ",myid)
outvol = [os.path.join(previousoutputdir,"vol%01d.hdf"%procid) for procid in xrange(2)]
for procid in xrange(2):
doit, keepchecking = checkstep(outvol[procid], keepchecking, myid, main_node)
if doit:
from multi_shc import do_volume
projdata = getindexdata(stack, partids[procid], partstack[procid], myid, nproc)
if ali3d_options.CTF: vol = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if( myid == main_node):
vol.write_image(outvol[procid])
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print( line,"Generated inivol #%01d "%procid)
del vol
if(myid == main_node):
if keepchecking:
procid = 1
if(os.path.join(mainoutputdir,"fusevol%01d.hdf"%procid)):
doit = 0
else:
doit = 1
keepchecking = False
else:
doit = 1
if doit:
vol = [get_im(outvol[procid]) for procid in xrange(2) ]
fq = 0.11 # which part to fuse
fuselowf(vol, fq)
for procid in xrange(2): vol[procid].write_image(os.path.join(mainoutputdir,"fusevol%01d.hdf"%procid) )
del vol
else: doit = 0
mpi_barrier(MPI_COMM_WORLD)
doit = bcast_number_to_all(doit, source_node = main_node)
# Refine two groups at a current resolution
lastring = int(shrink*radi + 0.5)
if(lastring < 2):
print( line,"ERROR!! lastring too small ", radi, shrink, lastring)
break
# REFINEMENT
# Part "a" SHC
for procid in xrange(2):
coutdir = os.path.join(mainoutputdir,"loga%01d"%procid)
doit, keepchecking = checkstep(coutdir , keepchecking, myid, main_node)
paramsdict = { "stack":stack,"delta":"%f"%round(degrees(atan(1.0/lastring)), 2) , "ts":"1", "xr":"2", "an":"-1", "center":options.center, "maxit":1500, \
"currentres":currentres, "aa":0.1, "radius":radi, "nsoft":1, "saturatecrit":0.75, "delpreviousmax":True, "shrink":shrink, \
"refvol":os.path.join(mainoutputdir,"fusevol%01d.hdf"%procid),"mask3D":options.mask3D }
if doit:
metamove(paramsdict, partids[procid], partstack[procid], coutdir, procid, myid, main_node, nproc)
partstack = [None]*2
for procid in xrange(2): partstack[procid] = os.path.join(mainoutputdir, "loga%01d"%procid, "params-chunk%01d.txt"%procid)
for procid in xrange(2):
outvol = os.path.join(mainoutputdir,"loga%01d"%procid,"shcvol%01d.hdf"%procid)
doit, keepchecking = checkstep(outvol, keepchecking, myid, main_node)
if doit:
from multi_shc import do_volume
projdata = getindexdata(stack, partids[procid], partstack[procid], myid, nproc)
if ali3d_options.CTF: vol = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if( myid == main_node):
vol.write_image(outvol)
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print( line,"Generated shcvol #%01d "%procid)
del vol
if(myid == main_node):
if keepchecking:
procid = 1
if(os.path.join(mainoutputdir,"loga%01d"%procid,"fusevol%01d.hdf"%procid) ):
doit = 0
else:
doit = 1
keepchecking = False
else:
doit = 1
if doit:
vol = []
for procid in xrange(2): vol.append(get_im(os.path.join(mainoutputdir,"loga%01d"%procid,"shcvol%01d.hdf"%procid) ))
fq = 0.11 # which part to fuse
fuselowf(vol, fq)
for procid in xrange(2): vol[procid].write_image( os.path.join(mainoutputdir,"loga%01d"%procid,"fusevol%01d.hdf"%procid) )
del vol
else: doit = 0
mpi_barrier(MPI_COMM_WORLD)
doit = bcast_number_to_all(doit, source_node = main_node)
# Part "b" deterministic
partstack = [None]*2
for procid in xrange(2): partstack[procid] = os.path.join(mainoutputdir,"loga%01d"%procid,"params-chunk%01d.txt"%procid)
for procid in xrange(2):
coutdir = os.path.join(mainoutputdir,"logb%01d"%procid)
doit, keepchecking = checkstep(coutdir, keepchecking, myid, main_node)
# Run exhaustive to finish up matching
paramsdict = { "stack":stack,"delta":"%f"%round(degrees(atan(1.0/lastring)), 2) , "ts":"1", "xr":"2", "an":"-1", "center":options.center, "maxit":10, \
"currentres":currentres, "aa":0.1, "radius":radi, "nsoft":0, "saturatecrit":0.95, "delpreviousmax":True, "shrink":shrink, \
"refvol":os.path.join(mainoutputdir,"loga%01d"%procid,"fusevol%01d.hdf"%procid), "mask3D":options.mask3D }
if doit:
metamove(paramsdict, partids[procid], partstack[procid], coutdir, procid, myid, main_node, nproc)
partstack = [None]*2
for procid in xrange(2): partstack[procid] = os.path.join(mainoutputdir,"logb%01d"%procid,"params-chunk%01d.txt"%procid)
# Compute current resolution, store result in main directory
doit, keepchecking = checkstep(os.path.join(mainoutputdir,"current_resolution.txt"), keepchecking, myid, main_node)
newres = 0.0
if doit:
newres = compute_resolution(stack, mainoutputdir, partids, partstack, radi, nnxo, ali3d_options.CTF, myid, main_node, nproc)
else:
if(myid == main_node): newres = read_text_file( os.path.join(mainoutputdir,"current_resolution.txt") )[0]
newres = bcast_number_to_all(newres, source_node = main_node)
# Here I have code to generate presentable results. IDs and params have to be merged and stored and an overall volume computed.
doit, keepchecking = checkstep(os.path.join(mainoutputdir,"volf.hdf"), keepchecking, myid, main_node)
if doit:
if( myid == main_node ):
pinids = map(int, read_text_file(partids[0]) ) + map(int, read_text_file(partids[1]) )
params = read_text_row(partstack[0]) + read_text_row(partstack[1])
assert(len(pinids) == len(params))
for i in xrange(len(pinids)):
pinids[i] = [ pinids[i], params[i] ]
del params
pinids.sort()
write_text_file([pinids[i][0] for i in xrange(len(pinids))], os.path.join(mainoutputdir,"indexes.txt"))
write_text_row( [pinids[i][1] for i in xrange(len(pinids))], os.path.join(mainoutputdir,"params.txt"))
mpi_barrier(MPI_COMM_WORLD)
ali3d_options.fl = newres
ali3d_options.ou = radi
projdata = getindexdata(stack, os.path.join(mainoutputdir,"indexes.txt"), os.path.join(mainoutputdir,"params.txt"), myid, nproc)
volf = do_volume(projdata, ali3d_options, mainiteration, mpi_comm = MPI_COMM_WORLD)
if(myid == main_node): volf.write_image(os.path.join(mainoutputdir,"volf.hdf"))
mpi_barrier(MPI_COMM_WORLD)
#print("RACING X ",myid)
if(newres == currentres):
for procid in xrange(2):
coutdir = os.path.join(mainoutputdir,"logc%01d"%procid)
doit, keepchecking = checkstep(coutdir, keepchecking, myid, main_node)
if doit:
# Do cross-check of the results
paramsdict = { "stack":stack,"delta":"%f"%round(degrees(atan(1.0/lastring)), 2) , "ts":"1", "xr":"2", "an":"-1", "center":options.center, "maxit":1, \
"currentres":newres, "aa":0.1, "radius":radi, "nsoft":0, "saturatecrit":0.95, "delpreviousmax":True, "shrink":shrink, \
"refvol":os.path.join(mainoutputdir,"vol%01d.hdf"%(1-procid)), "mask3D":options.mask3D }
# The cross-check uses parameters from step "b" to make sure shifts are correct.
# As the check is exhaustive, angles are ignored
metamove(paramsdict, partids[procid], partstack[procid], coutdir, procid, myid, main_node, nproc)
# identify bad apples
doit, keepchecking = checkstep(os.path.join(mainoutputdir,"badapples.txt"), keepchecking, myid, main_node)
if doit:
if(myid == main_node):
from utilities import get_symt
from pixel_error import max_3D_pixel_error
ts = get_symt(ali3d_options.sym)
badapples = []
deltaerror = 2.0
total_images_now = 0
for procid in xrange(2):
bad = []
ids = map(int,read_text_file( partids[procid] ))
total_images_now += len(ids)
oldp = read_text_row(partstack[procid])
newp = read_text_row(os.path.join(mainoutputdir,"logc%01d"%procid,"params-chunk%01d.txt"%procid))
for i in xrange(len(ids)):
t1 = Transform({"type":"spider","phi":oldp[i][0],"theta":oldp[i][1],"psi":oldp[i][2]})
t1.set_trans(Vec2f(-oldp[i][3]*shrink, -oldp[i][4]*shrink))
t2 = Transform({"type":"spider","phi":newp[i][0],"theta":newp[i][1],"psi":newp[i][2]})
t2.set_trans(Vec2f(-newp[i][3]*shrink, -newp[i][4]*shrink))
if(len(ts) > 1):
# only do it if it is not c1
pixel_error = +1.0e23
for kts in ts:
ut = t2*kts
# we do not care which position minimizes the error
pixel_error = min(max_3D_pixel_error(t1, ut, lastring), pixel_error)
else:
pixel_error = max_3D_pixel_error(t1, t2, lastring)
if(pixel_error > deltaerror):
bad.append(i)
if(len(bad)>0):
badapples += [ids[bad[i]] for i in xrange(len(bad))]
for i in xrange(len(bad)-1,-1,-1):
del oldp[bad[i]],ids[bad[i]]
if(len(ids) == 0):
ERROR("sxpetite","program diverged, all images have large angular errors, most likely the initial model is badly off",1)
else:
# This generate new parameters, hopefully to be used as starting ones in the new iteration
write_text_file(ids,os.path.join(mainoutputdir,"chunk%01d.txt"%procid))
write_text_row(oldp,os.path.join(mainoutputdir,"params-chunk%01d.txt"%procid))
if(len(badapples)>0):
badapples.sort()
write_text_file(badapples,os.path.join(mainoutputdir,"badapples.txt"))
eli = 100*float(len(badapples))/float(total_images_now)
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Elimination of outliers: %5.1f percent"%eli )
else: eli = 0.0
del badapples, oldp,ids,bad,newp,ts
else: eli =0.0
eli = bcast_number_to_all(eli, source_node = main_node)
# This part under MPI
if(eli > 0.0):
# Compute current resolution
depres = compute_resolution(stack, mainoutputdir, \
[os.path.join(mainoutputdir,"chunk%01d.txt"%procid) for procid in xrange(2)], \
[os.path.join(mainoutputdir,"params-chunk%01d.txt"%procid) for procid in xrange(2)], \
radi, nnxo, ali3d_options.CTF, myid, main_node, nproc)
depres = bcast_number_to_all(depres, source_node = main_node)
if(depres < newres):
# elimination of outliers decreased resolution, ignore the effort
eliminated_outliers = False
else:
eliminated_outliers = True
newres = depres
"""
# It does not seem to be needed, as data is there, we just point to the directory
for procid in xrange(2):
# set pointers to current parameters in main, which are for the reduced set stored above
partids[procid] = os.path.join(mainoutputdir,"chunk%01d.txt"%procid
partstack[procid] = os.path.join(mainoutputdir,"params-chunk%01d.txt"%procid)
"""
else:
eliminated_outliers = False
else:
eliminated_outliers = False
if(myid == main_node and not eliminated_outliers):
for procid in xrange(2):
# This is standard path, copy parameters to be used to the main
cmd = "{} {} {}".format("cp -p ", partids[procid] , os.path.join(mainoutputdir,"chunk%01d.txt"%procid))
cmdexecute(cmd)
cmd = "{} {} {}".format("cp -p ", partstack[procid], os.path.join(mainoutputdir,"params-chunk%01d.txt"%procid))
cmdexecute(cmd)
keepgoing = 0
if( newres > currentres or (eliminated_outliers and not tracker["eliminated-outliers"])):
if(myid == main_node): print(" Resolution improved, full steam ahead!")
if( newres > currentres ): tracker["movedup"] = True
else: tracker["movedup"] = False
shrink = max(min(2*newres + paramsdict["aa"], 1.0),minshrink)
tracker["extension"] = 4
nxshrink = min(int(nnxo*shrink + 0.5) + tracker["extension"],nnxo)
tracker["previous-resolution"] = newres
currentres = newres
tracker["bestsolution"] = mainiteration
bestoutputdir = mainoutputdir
tracker["eliminated-outliers"] = eliminated_outliers
keepgoing = 1
elif(newres < currentres):
if(not tracker["movedup"] and tracker["extension"] < 2 and mainiteration > 1):
keepgoing = 0
if(myid == main_node): print(" Cannot improve resolution, the best result is in the directory main%03d"%tracker["bestsolution"])
else:
if(not tracker["movedup"] and tracker["extension"] > 1 and mainiteration > 1):
if(myid == main_node): print(" Resolution decreased. Will decrease target resolution and will fall back on the best so far: main%03d"%tracker["bestsolution"])
bestoutputdir = os.path.join(masterdir,"main%03d"%tracker["bestsolution"])
elif( tracker["movedup"] and tracker["extension"] > 1 and mainiteration > 1):
if(myid == main_node): print(" Resolution decreased. Will decrease target resolution and will try starting from previous stage: main%03d"%(mainiteration - 1))
bestoutputdir = os.path.join(masterdir,"main%03d"%(mainiteration-1))
elif( mainiteration == 1):
if(myid == main_node): print(" Resolution decreased in the first iteration. It is expected, not to worry")
bestoutputdir = mainoutputdir
tracker["extension"] += 1
else: # missing something here?
if(myid == main_node): print(" Should not be here, ERROR 175!")
break
mpi_finalize()
exit()
if( bestoutputdir != mainoutputdir ):
# This is the key, we just reset the main to previous, so it will be eventually used as a starting in the next iteration
mainoutputdir = bestoutputdir
"""
# Set data from the main previous best to the current.
for procid in xrange(2):
partids[procid] = os.path.join(bestoutputdir,"chunk%01d.txt"%procid)
partstack[procid] = os.path.join(bestoutputdir,"params-chunk%01d.txt"%procid)
"""
if(myid == main_node):
currentres = read_text_file( os.path.join(bestoutputdir,"current_resolution.txt") )[0]
currentres = bcast_number_to_all(currentres, source_node = main_node)
shrink = max(min(2*currentres + paramsdict["aa"], 1.0), minshrink)
tracker["extension"] -= 1
nxshrink = min(int(nnxo*shrink + 0.5) + tracker["extension"],nnxo)
tracker["previous-resolution"] = newres
tracker["eliminated-outliers"] = eliminated_outliers
tracker["movedup"] = False
keepgoing = 1
elif(newres == currentres):
if( tracker["extension"] > 0 ):
if(myid == main_node): print("The resolution did not improve. This is look ahead move. Let's try to relax slightly and hope for the best")
tracker["extension"] -= 1
tracker["movedup"] = False
shrink = max(min(2*currentres + paramsdict["aa"], 1.0), minshrink)
nxshrink = min(int(nnxo*shrink + 0.5) + tracker["extension"],nnxo)
if( tracker["previous-nx"] == nnxo ):
keepgoing = 0
else:
tracker["previous-resolution"] = newres
currentres = newres
tracker["eliminated-outliers"] = eliminated_outliers
tracker["movedup"] = False
keepgoing = 1
else:
if(myid == main_node): print("The resolution did not improve.")
keepgoing = 0
if( keepgoing == 1 ):
if(myid == main_node):
print(" New shrink and image dimension :",shrink,nxshrink)
"""
# It does not look like it is necessary, we just have to point to the directory as the files should be there.
# Will continue, so update the params files
for procid in xrange(2):
# partids ads partstack contain parameters to be used as starting in the next iteration
if(not os.path.exists(os.path.join(mainoutputdir,"chunk%01d.txt"%procid))):
cmd = "{} {} {}".format("cp -p ", partids[procid] , os.path.join(mainoutputdir,"chunk%01d.txt"%procid))
cmdexecute(cmd)
if(not os.path.exists(os.path.join(mainoutputdir,"params-chunk%01d.txt"%procid))):
cmd = "{} {} {}".format("cp -p ", partstack[procid], os.path.join(mainoutputdir,"params-chunk%01d.txt"%procid))
cmdexecute(cmd)
"""
previousoutputdir = mainoutputdir
tracker["previous-shrink"] = shrink
tracker["previous-nx"] = nxshrink
else:
if(myid == main_node):
print(" Terminating, the best solution is in the directory main%03d"%tracker["bestsolution"])
mpi_barrier(MPI_COMM_WORLD)
mpi_finalize()
def main():
import global_def
from optparse import OptionParser
from EMAN2 import EMUtil
import os
import sys
from time import time
progname = os.path.basename(sys.argv[0])
usage = progname + " proj_stack output_averages --MPI"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--img_per_group",type="int" , default=100 , help="number of images per group" )
parser.add_option("--radius", type="int" , default=-1 , help="radius for alignment" )
parser.add_option("--xr", type="string" , default="2 1", help="range for translation search in x direction, search is +/xr")
parser.add_option("--yr", type="string" , default="-1", help="range for translation search in y direction, search is +/yr (default = same as xr)")
parser.add_option("--ts", type="string" , default="1 0.5", help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional")
parser.add_option("--iter", type="int" , default=30, help="number of iterations within alignment (default = 30)" )
parser.add_option("--num_ali", type="int" , default=5, help="number of alignments performed for stability (default = 5)" )
parser.add_option("--thld_err", type="float" , default=1.0, help="threshold of pixel error (default = 1.732)" )
parser.add_option("--grouping" , type="string" , default="GRP", help="do grouping of projections: PPR - per projection, GRP - different size groups, exclusive (default), GEV - grouping equal size")
parser.add_option("--delta", type="float" , default=-1.0, help="angular step for reference projections (required for GEV method)")
parser.add_option("--fl", type="float" , default=0.3, help="cut-off frequency of hyperbolic tangent low-pass Fourier filter")
parser.add_option("--aa", type="float" , default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter")
parser.add_option("--CTF", action="store_true", default=False, help="Consider CTF correction during the alignment ")
parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
(options,args) = parser.parse_args()
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD, MPI_TAG_UB
from mpi import mpi_barrier, mpi_send, mpi_recv, mpi_bcast, MPI_INT, mpi_finalize, MPI_FLOAT
from applications import MPI_start_end, within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import send_EMData, recv_EMData
from utilities import get_image, bcast_number_to_all, set_params2D, get_params2D
from utilities import group_proj_by_phitheta, model_circle, get_input_from_string
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 2:
stack = args[0]
outdir = args[1]
else:
ERROR("incomplete list of arguments", "sxproj_stability", 1, myid=myid)
exit()
if not options.MPI:
ERROR("Non-MPI not supported!", "sxproj_stability", myid=myid)
exit()
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
#if os.path.exists(outdir): ERROR('Output directory exists, please change the name and restart the program', "sxproj_stability", 1, myid)
#mpi_barrier(MPI_COMM_WORLD)
img_per_grp = options.img_per_group
radius = options.radius
ite = options.iter
num_ali = options.num_ali
thld_err = options.thld_err
xrng = get_input_from_string(options.xr)
if options.yr == "-1": yrng = xrng
else : yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
if radius == -1: radius = nx/2-2
mask = model_circle(radius, nx, nx)
st = time()
if options.grouping == "GRP":
if myid == main_node:
print " A ",myid," ",time()-st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp["psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
# Here is where the grouping is done, I didn't put enough annotation in the group_proj_by_phitheta,
# So I will briefly explain it here
# proj_list : Returns a list of list of particle numbers, each list contains img_per_grp particle numbers
# except for the last one. Depending on the number of particles left, they will either form a
# group or append themselves to the last group
# angle_list : Also returns a list of list, each list contains three numbers (phi, theta, delta), (phi,
# theta) is the projection angle of the center of the group, delta is the range of this group
# mirror_list: Also returns a list of list, each list contains img_per_grp True or False, which indicates
# whether it should take mirror position.
# In this program angle_list and mirror list are not of interest.
proj_list_all, angle_list, mirror_list = group_proj_by_phitheta(proj_params, img_per_grp=img_per_grp)
del proj_params
print " B number of groups ",myid," ",len(proj_list_all),time()-st
mpi_barrier(MPI_COMM_WORLD)
# Number of groups, actually there could be one or two more groups, since the size of the remaining group varies
# we will simply assign them to main node.
n_grp = nima/img_per_grp-1
# Divide proj_list_all equally to all nodes, and becomes proj_list
proj_list = []
for i in xrange(n_grp):
proc_to_stay = i%number_of_proc
if proc_to_stay == main_node:
if myid == main_node: proj_list.append(proj_list_all[i])
elif myid == main_node:
mpi_send(len(proj_list_all[i]), 1, MPI_INT, proc_to_stay, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(proj_list_all[i], len(proj_list_all[i]), MPI_INT, proc_to_stay, MPI_TAG_UB, MPI_COMM_WORLD)
elif myid == proc_to_stay:
img_per_grp = mpi_recv(1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
img_per_grp = int(img_per_grp[0])
temp = mpi_recv(img_per_grp, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
proj_list.append(map(int, temp))
del temp
mpi_barrier(MPI_COMM_WORLD)
print " C ",myid," ",time()-st
if myid == main_node:
# Assign the remaining groups to main_node
for i in xrange(n_grp, len(proj_list_all)):
proj_list.append(proj_list_all[i])
del proj_list_all, angle_list, mirror_list
# Compute stability per projection projection direction, equal number assigned, thus overlaps
elif options.grouping == "GEV":
if options.delta == -1.0: ERROR("Angular step for reference projections is required for GEV method","sxproj_stability",1)
from utilities import even_angles, nearestk_to_refdir, getvec
refproj = even_angles(options.delta)
img_begin, img_end = MPI_start_end(len(refproj), number_of_proc, myid)
# Now each processor keeps its own share of reference projections
refprojdir = refproj[img_begin: img_end]
del refproj
ref_ang = [0.0]*(len(refprojdir)*2)
for i in xrange(len(refprojdir)):
ref_ang[i*2] = refprojdir[0][0]
ref_ang[i*2+1] = refprojdir[0][1]+i*0.1
print " A ",myid," ",time()-st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
# the solution below is very slow, do not use it unless there is a problem with the i/O
"""
for i in xrange(number_of_proc):
if myid == i:
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
mpi_barrier(MPI_COMM_WORLD)
"""
print " B ",myid," ",time()-st
proj_ang = [0.0]*(nima*2)
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
proj_ang[i*2] = dp["phi"]
proj_ang[i*2+1] = dp["theta"]
print " C ",myid," ",time()-st
asi = Util.nearestk_to_refdir(proj_ang, ref_ang, img_per_grp)
del proj_ang, ref_ang
proj_list = []
for i in xrange(len(refprojdir)):
proj_list.append(asi[i*img_per_grp:(i+1)*img_per_grp])
del asi
print " D ",myid," ",time()-st
#from sys import exit
#exit()
# Compute stability per projection
elif options.grouping == "PPR":
print " A ",myid," ",time()-st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
print " B ",myid," ",time()-st
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp["psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
print " C ",myid," ",time()-st
from utilities import nearest_proj
proj_list, mirror_list = nearest_proj(proj_params, img_per_grp, range(img_begin, img_begin+1))#range(img_begin, img_end))
refprojdir = proj_params[img_begin: img_end]
del proj_params, mirror_list
print " D ",myid," ",time()-st
else: ERROR("Incorrect projection grouping option","sxproj_stability",1)
"""
from utilities import write_text_file
for i in xrange(len(proj_list)):
write_text_file(proj_list[i],"projlist%06d_%04d"%(i,myid))
"""
###########################################################################################################
# Begin stability test
from utilities import get_params_proj, read_text_file
#if myid == 0:
# from utilities import read_text_file
# proj_list[0] = map(int, read_text_file("lggrpp0.txt"))
from utilities import model_blank
aveList = [model_blank(nx,ny)]*len(proj_list)
if options.grouping == "GRP": refprojdir = [[0.0,0.0,-1.0]]*len(proj_list)
for i in xrange(len(proj_list)):
print " E ",myid," ",time()-st
class_data = EMData.read_images(stack, proj_list[i])
#print " R ",myid," ",time()-st
if options.CTF :
from filter import filt_ctf
for im in xrange(len(class_data)): # MEM LEAK!!
atemp = class_data[im].copy()
btemp = filt_ctf(atemp, atemp.get_attr("ctf"), binary=1)
class_data[im] = btemp
#class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
try:
t = im.get_attr("xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0,-d["tx"],-d["ty"],0,1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
#print " F ",myid," ",time()-st
# Here, we perform realignment num_ali times
all_ali_params = []
for j in xrange(num_ali):
if( xrng[0] == 0.0 and yrng[0] == 0.0 ):
avet = ali2d_ras(class_data, randomize = True, ir = 1, ou = radius, rs = 1, step = 1.0, dst = 90.0, maxit = ite, check_mirror = True, FH=options.fl, FF=options.aa)
else:
avet = within_group_refinement(class_data, mask, True, 1, radius, 1, xrng, yrng, step, 90.0, ite, options.fl, options.aa)
ali_params = []
for im in xrange(len(class_data)):
alpha, sx, sy, mirror, scale = get_params2D(class_data[im])
ali_params.extend( [alpha, sx, sy, mirror] )
all_ali_params.append(ali_params)
#aveList[i] = avet
#print " G ",myid," ",time()-st
del ali_params
# We determine the stability of this group here.
# stable_set contains all particles deemed stable, it is a list of list
# each list has two elements, the first is the pixel error, the second is the image number
# stable_set is sorted based on pixel error
#from utilities import write_text_file
#write_text_file(all_ali_params, "all_ali_params%03d.txt"%myid)
stable_set, mir_stab_rate, average_pix_err = multi_align_stability(all_ali_params, 0.0, 10000.0, thld_err, False, 2*radius+1)
#print " H ",myid," ",time()-st
if(len(stable_set) > 5):
stable_set_id = []
members = []
pix_err = []
# First put the stable members into attr 'members' and 'pix_err'
for s in stable_set:
# s[1] - number in this subset
stable_set_id.append(s[1])
# the original image number
members.append(proj_list[i][s[1]])
pix_err.append(s[0])
# Then put the unstable members into attr 'members' and 'pix_err'
from fundamentals import rot_shift2D
avet.to_zero()
if options.grouping == "GRP":
aphi = 0.0
atht = 0.0
vphi = 0.0
vtht = 0.0
l = -1
for j in xrange(len(proj_list[i])):
# Here it will only work if stable_set_id is sorted in the increasing number, see how l progresses
if j in stable_set_id:
l += 1
avet += rot_shift2D(class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3] )
if options.grouping == "GRP":
phi, theta, psi, sxs, sys = get_params_proj(class_data[j])
if( theta > 90.0):
phi = (phi+540.0)%360.0
theta = 180.0 - theta
aphi += phi
atht += theta
vphi += phi*phi
vtht += theta*theta
else:
members.append(proj_list[i][j])
pix_err.append(99999.99)
aveList[i] = avet.copy()
if l>1 :
l += 1
aveList[i] /= l
if options.grouping == "GRP":
aphi /= l
atht /= l
vphi = (vphi - l*aphi*aphi)/l
vtht = (vtht - l*atht*atht)/l
from math import sqrt
refprojdir[i] = [aphi, atht, (sqrt(max(vphi,0.0))+sqrt(max(vtht,0.0)))/2.0]
# Here more information has to be stored, PARTICULARLY WHAT IS THE REFERENCE DIRECTION
aveList[i].set_attr('members', members)
aveList[i].set_attr('refprojdir',refprojdir[i])
aveList[i].set_attr('pixerr', pix_err)
else:
print " empty group ",i, refprojdir[i]
aveList[i].set_attr('members',[-1])
aveList[i].set_attr('refprojdir',refprojdir[i])
aveList[i].set_attr('pixerr', [99999.])
del class_data
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node :
for im in xrange(len(aveList)):
aveList[im].write_image(args[1], km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im+i+70000)
nm = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('pixerr', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
ave.write_image(args[1], km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node,im+myid+70000)
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
members = aveList[im].get_attr('pixerr')
mpi_send(members, len(members), MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
global_def.BATCH = False
mpi_barrier(MPI_COMM_WORLD)
from mpi import mpi_finalize
mpi_finalize()
def do_volume_mrk02(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if (mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try:
local_filter = Tracker["local_filter"]
except:
local_filter = False
#=========================================================================
# volume reconstruction
if (type(data) == types.ListType):
if Tracker["constants"]["CTF"]:
vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if (Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(
int(Tracker["constants"]["radius"] * float(nx) /
float(Tracker["constants"]["nnxo"]) + 0.5), nx, nx, nx)
elif (Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if (type(Tracker["constants"]["mask3D"]) == types.StringType):
mask3D = get_im(Tracker["constants"]["mask3D"])
else:
mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if (nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(
rot_shift3D(mask3D, scale=float(nx) / float(nxm)), nx, nx,
nx)
nxm = mask3D.get_xsize()
assert (nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if (Tracker["PWadjustment"]):
from utilities import read_text_file, write_text_file
rt = read_text_file(Tracker["PWadjustment"])
fftip(vol)
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1, len(ro)):
ro[i] = (rt[i] / ro[i])**Tracker["upscale"]
#write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt")
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
from math import exp
for i in xrange(len(ro)): ro[i] *= \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
if local_filter:
# skip low-pass filtration
vol = fft(filt_table(vol, ro))
else:
if (type(Tracker["lowpass"]) == types.ListType):
vol = fft(
filt_table(filt_table(vol, Tracker["lowpass"]), ro))
else:
vol = fft(
filt_table(
filt_tanl(vol, Tracker["lowpass"],
Tracker["falloff"]), ro))
del ro
else:
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
ro = [0.0] * (ny // 2 + 2)
from math import exp
for i in xrange(len(ro)): ro[i] = \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
fftip(vol)
filt_table(vol, ro)
del ro
if not local_filter:
if (type(Tracker["lowpass"]) == types.ListType):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"],
Tracker["falloff"])
if Tracker["constants"]["sausage"]: vol = fft(vol)
if local_filter:
from morphology import binarize
if (myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node=0)
# only main processor needs the two input volumes
if (myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if (lx != nx):
if (lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(
rot_shift3D(mask, scale=float(lx) / float(nx)), lx, lx,
lx)
vol = fdecimate(vol, lx, lx, lx)
else:
ERROR("local filter cannot be larger than input volume",
"user function", 1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
else:
lx = 0
locres = model_blank(1, 1, 1)
vol = model_blank(1, 1, 1)
lx = bcast_number_to_all(lx, source_node=0)
if (myid != 0): mask = model_blank(lx, lx, lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal(locres, vol, mask, Tracker["falloff"], myid, 0,
nproc)
if myid == 0:
if (lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx, nx, nx)
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx, nx, nx)
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0 / stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5) # This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def main():
def params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror):
if mirror:
m = 1
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)
else:
m = 0
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)
return alpha, sx, sy, m
progname = os.path.basename(sys.argv[0])
usage = progname + " prj_stack --ave2D= --var2D= --ave3D= --var3D= --img_per_grp= --fl=0.2 --aa=0.1 --sym=symmetry --CTF"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--ave2D", type="string" , default=False, help="write to the disk a stack of 2D averages")
parser.add_option("--var2D", type="string" , default=False, help="write to the disk a stack of 2D variances")
parser.add_option("--ave3D", type="string" , default=False, help="write to the disk reconstructed 3D average")
parser.add_option("--var3D", type="string" , default=False, help="compute 3D variability (time consuming!)")
parser.add_option("--img_per_grp", type="int" , default=10 , help="number of neighbouring projections")
parser.add_option("--no_norm", action="store_true", default=False, help="do not use normalization")
parser.add_option("--radiusvar", type="int" , default=-1 , help="radius for 3D var" )
parser.add_option("--npad", type="int" , default=2 , help="number of time to pad the original images")
parser.add_option("--sym" , type="string" , default="c1" , help="symmetry")
parser.add_option("--fl", type="float" , default=0.0 , help="stop-band frequency (Default - no filtration)")
parser.add_option("--aa", type="float" , default=0.0 , help="fall off of the filter (Default - no filtration)")
parser.add_option("--CTF", action="store_true", default=False, help="use CFT correction")
parser.add_option("--VERBOSE", action="store_true", default=False, help="Long output for debugging")
#parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
#parser.add_option("--radiuspca", type="int" , default=-1 , help="radius for PCA" )
#parser.add_option("--iter", type="int" , default=40 , help="maximum number of iterations (stop criterion of reconstruction process)" )
#parser.add_option("--abs", type="float" , default=0.0 , help="minimum average absolute change of voxels' values (stop criterion of reconstruction process)" )
#parser.add_option("--squ", type="float" , default=0.0 , help="minimum average squared change of voxels' values (stop criterion of reconstruction process)" )
parser.add_option("--VAR" , action="store_true", default=False, help="stack on input consists of 2D variances (Default False)")
parser.add_option("--decimate", type="float", default=1.0, help="image decimate rate, a number large than 1. default is 1")
parser.add_option("--window", type="int", default=0, help="reduce images to a small image size without changing pixel_size. Default value is zero.")
#parser.add_option("--SND", action="store_true", default=False, help="compute squared normalized differences (Default False)")
parser.add_option("--nvec", type="int" , default=0 , help="number of eigenvectors, default = 0 meaning no PCA calculated")
parser.add_option("--symmetrize", action="store_true", default=False, help="Prepare input stack for handling symmetry (Default False)")
(options,args) = parser.parse_args()
#####
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, mpi_recv, MPI_COMM_WORLD, MPI_TAG_UB
from mpi import mpi_barrier, mpi_reduce, mpi_bcast, mpi_send, MPI_FLOAT, MPI_SUM, MPI_INT, MPI_MAX
from applications import MPI_start_end
from reconstruction import recons3d_em, recons3d_em_MPI
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import print_begin_msg, print_end_msg, print_msg
from utilities import read_text_row, get_image, get_im
from utilities import bcast_EMData_to_all, bcast_number_to_all
from utilities import get_symt
# This is code for handling symmetries by the above program. To be incorporated. PAP 01/27/2015
from EMAN2db import db_open_dict
if options.symmetrize :
try:
sys.argv = mpi_init(len(sys.argv), sys.argv)
try:
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
if( number_of_proc > 1 ):
ERROR("Cannot use more than one CPU for symmetry prepration","sx3dvariability",1)
except:
pass
except:
pass
# Input
#instack = "Clean_NORM_CTF_start_wparams.hdf"
#instack = "bdb:data"
instack = args[0]
sym = options.sym
if( sym == "c1" ):
ERROR("Thre is no need to symmetrize stack for C1 symmetry","sx3dvariability",1)
if(instack[:4] !="bdb:"):
stack = "bdb:data"
delete_bdb(stack)
cmdexecute("sxcpy.py "+instack+" "+stack)
else:
stack = instack
qt = EMUtil.get_all_attributes(stack,'xform.projection')
na = len(qt)
ts = get_symt(sym)
ks = len(ts)
angsa = [None]*na
for k in xrange(ks):
delete_bdb("bdb:Q%1d"%k)
cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
DB = db_open_dict("bdb:Q%1d"%k)
for i in xrange(na):
ut = qt[i]*ts[k]
DB.set_attr(i, "xform.projection", ut)
#bt = ut.get_params("spider")
#angsa[i] = [round(bt["phi"],3)%360.0, round(bt["theta"],3)%360.0, bt["psi"], -bt["tx"], -bt["ty"]]
#write_text_row(angsa, 'ptsma%1d.txt'%k)
#cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
#cmdexecute("sxheader.py bdb:Q%1d --params=xform.projection --import=ptsma%1d.txt"%(k,k))
DB.close()
delete_bdb("bdb:sdata")
cmdexecute("e2bdb.py . --makevstack=bdb:sdata --filt=Q")
#cmdexecute("ls EMAN2DB/sdata*")
a = get_im("bdb:sdata")
a.set_attr("variabilitysymmetry",sym)
a.write_image("bdb:sdata")
else:
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 1:
stack = args[0]
else:
print( "usage: " + usage)
print( "Please run '" + progname + " -h' for detailed options")
return 1
t0 = time()
# obsolete flags
options.MPI = True
options.nvec = 0
options.radiuspca = -1
options.iter = 40
options.abs = 0.0
options.squ = 0.0
if options.fl > 0.0 and options.aa == 0.0:
ERROR("Fall off has to be given for the low-pass filter", "sx3dvariability", 1, myid)
if options.VAR and options.SND:
ERROR("Only one of var and SND can be set!", "sx3dvariability", myid)
exit()
if options.VAR and (options.ave2D or options.ave3D or options.var2D):
ERROR("When VAR is set, the program cannot output ave2D, ave3D or var2D", "sx3dvariability", 1, myid)
exit()
#if options.SND and (options.ave2D or options.ave3D):
# ERROR("When SND is set, the program cannot output ave2D or ave3D", "sx3dvariability", 1, myid)
# exit()
if options.nvec > 0 :
ERROR("PCA option not implemented", "sx3dvariability", 1, myid)
exit()
if options.nvec > 0 and options.ave3D == None:
ERROR("When doing PCA analysis, one must set ave3D", "sx3dvariability", myid=myid)
exit()
import string
options.sym = options.sym.lower()
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
if myid == main_node:
print_begin_msg("sx3dvariability")
print_msg("%-70s: %s\n"%("Input stack", stack))
img_per_grp = options.img_per_grp
nvec = options.nvec
radiuspca = options.radiuspca
symbaselen = 0
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
if options.sym != "c1" :
imgdata = get_im(stack)
try:
i = imgdata.get_attr("variabilitysymmetry")
if(i != options.sym):
ERROR("The symmetry provided does not agree with the symmetry of the input stack", "sx3dvariability", myid=myid)
except:
ERROR("Input stack is not prepared for symmetry, please follow instructions", "sx3dvariability", myid=myid)
from utilities import get_symt
i = len(get_symt(options.sym))
if((nima/i)*i != nima):
ERROR("The length of the input stack is incorrect for symmetry processing", "sx3dvariability", myid=myid)
symbaselen = nima/i
else: symbaselen = nima
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
Tracker ={}
Tracker["nx"] =nx
Tracker["ny"] =ny
Tracker["total_stack"]=nima
if options.decimate==1.:
if options.window !=0:
nx = options.window
ny = options.window
else:
if options.window ==0:
nx = int(nx/options.decimate)
ny = int(ny/options.decimate)
else:
nx = int(options.window/options.decimate)
ny = nx
symbaselen = bcast_number_to_all(symbaselen)
if radiuspca == -1: radiuspca = nx/2-2
if myid == main_node:
print_msg("%-70s: %d\n"%("Number of projection", nima))
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
"""
if options.SND:
from projection import prep_vol, prgs
from statistics import im_diff
from utilities import get_im, model_circle, get_params_proj, set_params_proj
from utilities import get_ctf, generate_ctf
from filter import filt_ctf
imgdata = EMData.read_images(stack, range(img_begin, img_end))
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
bcast_EMData_to_all(vol, myid)
volft, kb = prep_vol(vol)
mask = model_circle(nx/2-2, nx, ny)
varList = []
for i in xrange(img_begin, img_end):
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[i-img_begin])
ref_prj = prgs(volft, kb, [phi, theta, psi, -s2x, -s2y])
if options.CTF:
ctf_params = get_ctf(imgdata[i-img_begin])
ref_prj = filt_ctf(ref_prj, generate_ctf(ctf_params))
diff, A, B = im_diff(ref_prj, imgdata[i-img_begin], mask)
diff2 = diff*diff
set_params_proj(diff2, [phi, theta, psi, s2x, s2y])
varList.append(diff2)
mpi_barrier(MPI_COMM_WORLD)
"""
if options.VAR:
#varList = EMData.read_images(stack, range(img_begin, img_end))
varList = []
this_image = EMData()
for index_of_particle in xrange(img_begin,img_end):
this_image.read_image(stack,index_of_particle)
varList.append(image_decimate_window_xform_ctf(img,options.decimate,options.window,options.CTF))
else:
from utilities import bcast_number_to_all, bcast_list_to_all, send_EMData, recv_EMData
from utilities import set_params_proj, get_params_proj, params_3D_2D, get_params2D, set_params2D, compose_transform2
from utilities import model_blank, nearest_proj, model_circle
from applications import pca
from statistics import avgvar, avgvar_ctf, ccc
from filter import filt_tanl
from morphology import threshold, square_root
from projection import project, prep_vol, prgs
from sets import Set
if myid == main_node:
t1 = time()
proj_angles = []
aveList = []
tab = EMUtil.get_all_attributes(stack, 'xform.projection')
for i in xrange(nima):
t = tab[i].get_params('spider')
phi = t['phi']
theta = t['theta']
psi = t['psi']
x = theta
if x > 90.0: x = 180.0 - x
x = x*10000+psi
proj_angles.append([x, t['phi'], t['theta'], t['psi'], i])
t2 = time()
print_msg("%-70s: %d\n"%("Number of neighboring projections", img_per_grp))
print_msg("...... Finding neighboring projections\n")
if options.VERBOSE:
print "Number of images per group: ", img_per_grp
print "Now grouping projections"
proj_angles.sort()
proj_angles_list = [0.0]*(nima*4)
if myid == main_node:
for i in xrange(nima):
proj_angles_list[i*4] = proj_angles[i][1]
proj_angles_list[i*4+1] = proj_angles[i][2]
proj_angles_list[i*4+2] = proj_angles[i][3]
proj_angles_list[i*4+3] = proj_angles[i][4]
proj_angles_list = bcast_list_to_all(proj_angles_list, myid, main_node)
proj_angles = []
for i in xrange(nima):
proj_angles.append([proj_angles_list[i*4], proj_angles_list[i*4+1], proj_angles_list[i*4+2], int(proj_angles_list[i*4+3])])
del proj_angles_list
proj_list, mirror_list = nearest_proj(proj_angles, img_per_grp, range(img_begin, img_end))
all_proj = Set()
for im in proj_list:
for jm in im:
all_proj.add(proj_angles[jm][3])
all_proj = list(all_proj)
if options.VERBOSE:
print "On node %2d, number of images needed to be read = %5d"%(myid, len(all_proj))
index = {}
for i in xrange(len(all_proj)): index[all_proj[i]] = i
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
print_msg("%-70s: %.2f\n"%("Finding neighboring projections lasted [s]", time()-t2))
print_msg("%-70s: %d\n"%("Number of groups processed on the main node", len(proj_list)))
if options.VERBOSE:
print "Grouping projections took: ", (time()-t2)/60 , "[min]"
print "Number of groups on main node: ", len(proj_list)
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
print_msg("...... calculating the stack of 2D variances \n")
if options.VERBOSE:
print "Now calculating the stack of 2D variances"
proj_params = [0.0]*(nima*5)
aveList = []
varList = []
if nvec > 0:
eigList = [[] for i in xrange(nvec)]
if options.VERBOSE: print "Begin to read images on processor %d"%(myid)
ttt = time()
#imgdata = EMData.read_images(stack, all_proj)
img = EMData()
imgdata = []
for index_of_proj in xrange(len(all_proj)):
img.read_image(stack, all_proj[index_of_proj])
dmg = image_decimate_window_xform_ctf(img,options.decimate,options.window,options.CTF)
#print dmg.get_xsize(), "init"
imgdata.append(dmg)
if options.VERBOSE:
print "Reading images on processor %d done, time = %.2f"%(myid, time()-ttt)
print "On processor %d, we got %d images"%(myid, len(imgdata))
mpi_barrier(MPI_COMM_WORLD)
'''
imgdata2 = EMData.read_images(stack, range(img_begin, img_end))
if options.fl > 0.0:
for k in xrange(len(imgdata2)):
imgdata2[k] = filt_tanl(imgdata2[k], options.fl, options.aa)
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata2, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata2, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
if myid == main_node:
vol.write_image("vol_ctf.hdf")
print_msg("Writing to the disk volume reconstructed from averages as : %s\n"%("vol_ctf.hdf"))
del vol, imgdata2
mpi_barrier(MPI_COMM_WORLD)
'''
from applications import prepare_2d_forPCA
from utilities import model_blank
for i in xrange(len(proj_list)):
ki = proj_angles[proj_list[i][0]][3]
if ki >= symbaselen: continue
mi = index[ki]
phiM, thetaM, psiM, s2xM, s2yM = get_params_proj(imgdata[mi])
grp_imgdata = []
for j in xrange(img_per_grp):
mj = index[proj_angles[proj_list[i][j]][3]]
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[mj])
alpha, sx, sy, mirror = params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror_list[i][j])
if thetaM <= 90:
if mirror == 0: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, phiM-phi, 0.0, 0.0, 1.0)
else: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, 180-(phiM-phi), 0.0, 0.0, 1.0)
else:
if mirror == 0: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(phiM-phi), 0.0, 0.0, 1.0)
else: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(180-(phiM-phi)), 0.0, 0.0, 1.0)
set_params2D(imgdata[mj], [alpha, sx, sy, mirror, 1.0])
grp_imgdata.append(imgdata[mj])
#print grp_imgdata[j].get_xsize(), imgdata[mj].get_xsize()
if not options.no_norm:
#print grp_imgdata[j].get_xsize()
mask = model_circle(nx/2-2, nx, nx)
for k in xrange(img_per_grp):
ave, std, minn, maxx = Util.infomask(grp_imgdata[k], mask, False)
grp_imgdata[k] -= ave
grp_imgdata[k] /= std
del mask
if options.fl > 0.0:
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2*nx
ny2 = 2*ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d(fft( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa) ),nx,ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
for k in xrange(img_per_grp):
grp_imgdata[k] = filt_tanl( grp_imgdata[k], options.fl, options.aa)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
else:
from utilities import pad, read_text_file
from filter import filt_ctf, filt_table
from fundamentals import fft, window2d
nx2 = 2*nx
ny2 = 2*ny
if options.CTF:
from utilities import pad
for k in xrange(img_per_grp):
grp_imgdata[k] = window2d( fft( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1) ) , nx,ny)
#grp_imgdata[k] = window2d(fft( filt_table( filt_tanl( filt_ctf(fft(pad(grp_imgdata[k], nx2, ny2, 1,0.0)), grp_imgdata[k].get_attr("ctf"), binary=1), options.fl, options.aa), fifi) ),nx,ny)
#grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
'''
if i < 10 and myid == main_node:
for k in xrange(10):
grp_imgdata[k].write_image("grp%03d.hdf"%i, k)
'''
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("pp.hdf", pp)
"""
ave, grp_imgdata = prepare_2d_forPCA(grp_imgdata)
"""
if myid == main_node and i==0:
for pp in xrange(len(grp_imgdata)):
grp_imgdata[pp].write_image("qq.hdf", pp)
"""
var = model_blank(nx,ny)
for q in grp_imgdata: Util.add_img2( var, q )
Util.mul_scalar( var, 1.0/(len(grp_imgdata)-1))
# Switch to std dev
var = square_root(threshold(var))
#if options.CTF: ave, var = avgvar_ctf(grp_imgdata, mode="a")
#else: ave, var = avgvar(grp_imgdata, mode="a")
"""
if myid == main_node:
ave.write_image("avgv.hdf",i)
var.write_image("varv.hdf",i)
"""
set_params_proj(ave, [phiM, thetaM, 0.0, 0.0, 0.0])
set_params_proj(var, [phiM, thetaM, 0.0, 0.0, 0.0])
aveList.append(ave)
varList.append(var)
if options.VERBOSE:
print "%5.2f%% done on processor %d"%(i*100.0/len(proj_list), myid)
if nvec > 0:
eig = pca(input_stacks=grp_imgdata, subavg="", mask_radius=radiuspca, nvec=nvec, incore=True, shuffle=False, genbuf=True)
for k in xrange(nvec):
set_params_proj(eig[k], [phiM, thetaM, 0.0, 0.0, 0.0])
eigList[k].append(eig[k])
"""
if myid == 0 and i == 0:
for k in xrange(nvec):
eig[k].write_image("eig.hdf", k)
"""
del imgdata
# To this point, all averages, variances, and eigenvectors are computed
if options.ave2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node :
for im in xrange(len(aveList)):
aveList[im].write_image(options.ave2D, km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im+i+70000)
"""
nm = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('pix_err', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, MPI_TAG_UB, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
"""
tmpvol=fpol(ave, Tracker["nx"],Tracker["nx"],Tracker["nx"])
tmpvol.write_image(options.ave2D, km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node,im+myid+70000)
"""
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
members = aveList[im].get_attr('pix_err')
mpi_send(members, len(members), MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
"""
if options.ave3D:
from fundamentals import fpol
if options.VERBOSE:
print "Reconstructing 3D average volume"
ave3D = recons3d_4nn_MPI(myid, aveList, symmetry=options.sym, npad=options.npad)
bcast_EMData_to_all(ave3D, myid)
if myid == main_node:
ave3D=fpol(ave3D,Tracker["nx"],Tracker["nx"],Tracker["nx"])
ave3D.write_image(options.ave3D)
print_msg("%-70s: %s\n"%("Writing to the disk volume reconstructed from averages as", options.ave3D))
del ave, var, proj_list, stack, phi, theta, psi, s2x, s2y, alpha, sx, sy, mirror, aveList
if nvec > 0:
for k in xrange(nvec):
if options.VERBOSE:
print "Reconstruction eigenvolumes", k
cont = True
ITER = 0
mask2d = model_circle(radiuspca, nx, nx)
while cont:
#print "On node %d, iteration %d"%(myid, ITER)
eig3D = recons3d_4nn_MPI(myid, eigList[k], symmetry=options.sym, npad=options.npad)
bcast_EMData_to_all(eig3D, myid, main_node)
if options.fl > 0.0:
eig3D = filt_tanl(eig3D, options.fl, options.aa)
if myid == main_node:
eig3D.write_image("eig3d_%03d.hdf"%k, ITER)
Util.mul_img( eig3D, model_circle(radiuspca, nx, nx, nx) )
eig3Df, kb = prep_vol(eig3D)
del eig3D
cont = False
icont = 0
for l in xrange(len(eigList[k])):
phi, theta, psi, s2x, s2y = get_params_proj(eigList[k][l])
proj = prgs(eig3Df, kb, [phi, theta, psi, s2x, s2y])
cl = ccc(proj, eigList[k][l], mask2d)
if cl < 0.0:
icont += 1
cont = True
eigList[k][l] *= -1.0
u = int(cont)
u = mpi_reduce([u], 1, MPI_INT, MPI_MAX, main_node, MPI_COMM_WORLD)
icont = mpi_reduce([icont], 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
u = int(u[0])
print " Eigenvector: ",k," number changed ",int(icont[0])
else: u = 0
u = bcast_number_to_all(u, main_node)
cont = bool(u)
ITER += 1
del eig3Df, kb
mpi_barrier(MPI_COMM_WORLD)
del eigList, mask2d
if options.ave3D: del ave3D
if options.var2D:
from fundamentals import fpol
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node :
for im in xrange(len(varList)):
tmpvol=fpol(varList[im], Tracker["nx"], Tracker["nx"],1)
tmpvol.write_image(options.var2D, km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, MPI_TAG_UB, MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im+i+70000)
tmpvol=fpol(ave, Tracker["nx"], Tracker["nx"],1)
tmpvol.write_image(options.var2D, km)
km += 1
else:
mpi_send(len(varList), 1, MPI_INT, main_node, MPI_TAG_UB, MPI_COMM_WORLD)
for im in xrange(len(varList)):
send_EMData(varList[im], main_node, im+myid+70000)# What with the attributes??
mpi_barrier(MPI_COMM_WORLD)
if options.var3D:
if myid == main_node and options.VERBOSE:
print "Reconstructing 3D variability volume"
t6 = time()
radiusvar = options.radiusvar
if( radiusvar < 0 ): radiusvar = nx//2 -3
res = recons3d_4nn_MPI(myid, varList, symmetry=options.sym, npad=options.npad)
#res = recons3d_em_MPI(varList, vol_stack, options.iter, radiusvar, options.abs, True, options.sym, options.squ)
if myid == main_node:
from fundamentals import fpol
res =fpol(res, Tracker["nx"], Tracker["nx"], Tracker["nx"])
res.write_image(options.var3D)
if myid == main_node:
print_msg("%-70s: %.2f\n"%("Reconstructing 3D variability took [s]", time()-t6))
if options.VERBOSE:
print "Reconstruction took: %.2f [min]"%((time()-t6)/60)
if myid == main_node:
print_msg("%-70s: %.2f\n"%("Total time for these computations [s]", time()-t0))
if options.VERBOSE:
print "Total time for these computations: %.2f [min]"%((time()-t0)/60)
print_end_msg("sx3dvariability")
global_def.BATCH = False
from mpi import mpi_finalize
mpi_finalize()
def compute_fscs(stack, outputdir, chunkname, newgoodname, fscoutputdir, doit, keepchecking, nproc, myid, main_node):
# Compute reconstructions per group from good particles only to get FSC curves
# We will compute two FSC curves - from not averaged parameters and from averaged parameters
# So, we have to build two sets:
# not averaged (A2+C3) versus (B0+D5)
# averaged (A0+C1) versus (B3+D4)
# This requires pulling good subsets given by goodX*; I am not sure why good, sxconsistency above produced newgood text files.
# Otherwise, I am not sure what newbad will contain.
# Input that should vary:
# "bdb:"+os.path.join(outputdir,"chunk%01d%01d"%(procid,i))
# os.path.join(outputdir,"newgood%01d.txt"%procid)
# Output should be in a separate directory "fscoutputdir"
if(myid == main_node):
if keepchecking:
if(os.path.exists(fscoutputdir)):
doit = 0
print("Directory ",fscoutputdir," exists!")
else:
doit = 1
keepchecking = False
else:
doit = 1
if doit:
cmd = "{} {}".format("mkdir", fscoutputdir)
cmdexecute(cmd)
mpi_barrier(MPI_COMM_WORLD)
# not averaged
doit, keepchecking = checkstep(os.path.join(fscoutputdir,"volfscn0.hdf"), keepchecking, myid, main_node)
if doit:
if(myid == main_node):
# A2+C3
# indices
write_text_file( \
map(int, read_text_file(os.path.join(outputdir,chunkname+"0.txt")))+map(int, read_text_file(os.path.join(outputdir,chunkname+"2.txt"))), \
os.path.join(fscoutputdir,"chunkfn0.txt"))
# params
write_text_row( \
read_text_row(os.path.join(outputdir,newgoodname+"02.txt"))+read_text_row(os.path.join(outputdir,newgoodname+"22.txt")), \
os.path.join(fscoutputdir,"params-chunkfn0.txt"))
mpi_barrier(MPI_COMM_WORLD)
projdata = getindexdata(stack, os.path.join(fscoutputdir,"chunkfn0.txt"), os.path.join(fscoutputdir,"params-chunkfn0.txt"), myid, nproc)
if ali3d_options.CTF: vol = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if(myid == main_node):
vol.write_image(os.path.join(fscoutputdir,"volfscn0.hdf"))
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Executed successfully: 3D reconstruction of", os.path.join(fscoutputdir,"volfscn0.hdf"))
del vol
doit, keepchecking = checkstep(os.path.join(fscoutputdir,"volfscn1.hdf"), keepchecking, myid, main_node)
if doit:
if(myid == main_node):
# B0+D5
# indices
write_text_file( \
map(int, read_text_file(os.path.join(outputdir,chunkname+"1.txt")))+map(int, read_text_file(os.path.join(outputdir,chunkname+"3.txt"))), \
os.path.join(fscoutputdir,"chunkfn1.txt"))
# params
write_text_row( \
read_text_row(os.path.join(outputdir,newgoodname+"10.txt"))+read_text_row(os.path.join(outputdir,newgoodname+"32.txt")), \
os.path.join(fscoutputdir,"params-chunkfn1.txt"))
mpi_barrier(MPI_COMM_WORLD)
projdata = getindexdata(stack, os.path.join(fscoutputdir,"chunkfn1.txt"), os.path.join(fscoutputdir,"params-chunkfn1.txt"), myid, nproc)
if ali3d_options.CTF: vol = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if(myid == main_node):
vol.write_image(os.path.join(fscoutputdir,"volfscn1.hdf"))
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Executed successfully: 3D reconstruction of", os.path.join(fscoutputdir,"volfscn1.hdf"))
del vol
# averaged
doit, keepchecking = checkstep(os.path.join(fscoutputdir,"volfsca0.hdf"), keepchecking, myid, main_node)
if doit:
if(myid == main_node):
# A0+C1
# indices
write_text_file( \
map(int, read_text_file(os.path.join(outputdir,chunkname+"0.txt")))+map(int, read_text_file(os.path.join(outputdir,chunkname+"2.txt"))), \
os.path.join(fscoutputdir,"chunkfa0.txt"))
# params
write_text_row( \
read_text_row(os.path.join(outputdir,newgoodname+"00.txt"))+read_text_row(os.path.join(outputdir,newgoodname+"20.txt")), \
os.path.join(fscoutputdir,"params-chunkfa0.txt"))
mpi_barrier(MPI_COMM_WORLD)
projdata = getindexdata(stack, os.path.join(fscoutputdir,"chunkfa0.txt"), os.path.join(fscoutputdir,"params-chunkfa0.txt"), myid, nproc)
if ali3d_options.CTF: vol = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if(myid == main_node):
vol.write_image(os.path.join(fscoutputdir,"volfsca0.hdf"))
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Executed successfully: 3D reconstruction of", os.path.join(fscoutputdir,"volfsca0.hdf"))
del vol
doit, keepchecking = checkstep(os.path.join(fscoutputdir,"volfsca1.hdf"), keepchecking, myid, main_node)
if doit:
if(myid == main_node):
# B3+D4
write_text_file( \
map(int, read_text_file(os.path.join(outputdir,chunkname+"1.txt")))+map(int, read_text_file(os.path.join(outputdir,chunkname+"3.txt"))), \
os.path.join(fscoutputdir,"chunkfa1.txt"))
# params
write_text_row( \
read_text_row(os.path.join(outputdir,newgoodname+"11.txt"))+read_text_row(os.path.join(outputdir,newgoodname+"31.txt")), \
os.path.join(fscoutputdir,"params-chunkfa1.txt"))
mpi_barrier(MPI_COMM_WORLD)
projdata = getindexdata(stack, os.path.join(fscoutputdir,"chunkfa1.txt"), os.path.join(fscoutputdir,"params-chunkfa1.txt"), myid, nproc)
if ali3d_options.CTF: vol = recons3d_4nn_ctf_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
else: vol = recons3d_4nn_MPI(myid, projdata, symmetry=ali3d_options.sym, npad = 2)
del projdata
if(myid == main_node):
vol.write_image(os.path.join(fscoutputdir,"volfsca1.hdf"))
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Executed successfully: 3D reconstruction of", os.path.join(fscoutputdir,"volfsca1.hdf"))
del vol
# Get updated FSC curves
if(myid == main_node):
if(ali3d_options.mask3D is None): mask = model_circle(radi,nnxo,nnxo,nnxo)
else:
mask = get_im(ali3d_options.mask3D)
if keepchecking:
if(os.path.exists(os.path.join(fscoutputdir,"fscn.txt"))):
doit = 0
else:
doit = 1
keepchecking = False
else: doit = 1
if doit: fsc(get_im(os.path.join(fscoutputdir,"volfscn0.hdf"))*mask,\
get_im(os.path.join(fscoutputdir,"volfscn1.hdf"))*mask,\
1.0,os.path.join(fscoutputdir,"fscn.txt") )
if keepchecking:
if(os.path.exists(os.path.join(fscoutputdir,"fsca.txt"))):
doit = 0
else:
doit = 1
keepchecking = False
else: doit = 1
if doit: fsc(get_im(os.path.join(fscoutputdir,"volfsca0.hdf"))*mask,\
get_im(os.path.join(fscoutputdir,"volfsca1.hdf"))*mask,\
1.0,os.path.join(fscoutputdir,"fsca.txt") )
nfsc = read_text_file(os.path.join(fscoutputdir,"fscn.txt") ,-1)
currentres = 0.5
ns = len(nfsc[1])
for i in xrange(1,ns-1):
if ( (2*nfsc[1][i]/(1.0+nfsc[1][i]) ) < 0.5):
currentres = nfsc[0][i-1]
break
print(" Current resolution ",i,currentres)
else:
currentres = 0.0
currentres = bcast_number_to_all(currentres, source_node = main_node)
if(currentres < 0.0):
if(myid == main_node):
print(" Something wrong with the resolution, cannot continue")
mpi_finalize()
exit()
mpi_barrier(MPI_COMM_WORLD)
return currentres, doit, keepchecking
def runcheck(classavgstack, reconfile, outdir, inangles=None, selectdoc=None, prjmethod='trilinear', displayYN=False,
projstack='proj.hdf', outangles='angles.txt', outstack='comp-proj-reproj.hdf', normstack='comp-proj-reproj-norm.hdf'):
print("\n%s, Modified 2018-12-07\n" % __file__)
# Check if inputs exist
check(classavgstack)
check(reconfile)
# Create directory if it doesn't exist
if not os.path.isdir(outdir):
os.makedirs(outdir) # os.mkdir() can only operate one directory deep
print("mkdir -p %s" % outdir)
# Expand path for outputs
projstack = os.path.join(outdir, projstack)
outangles = os.path.join(outdir, outangles)
outstack = os.path.join(outdir, outstack)
normstack = os.path.join(outdir, normstack)
# Get number of images
nimg0 = EMAN2_cppwrap.EMUtil.get_image_count(classavgstack)
recon = EMAN2_cppwrap.EMData(reconfile)
nx = recon.get_xsize()
# In case class averages include discarded images, apply selection file
if selectdoc:
goodavgs, extension = os.path.splitext(classavgstack)
newclasses = goodavgs + "_kept" + extension
# e2proc2d appends to existing files, so rename existing output
if os.path.exists(newclasses):
renamefile = newclasses + '.bak'
os.rename(newclasses, renamefile)
print("mv %s %s" % (newclasses, renamefile))
cmd7="e2proc2d.py %s %s --list=%s" % (classavgstack, newclasses, selectdoc)
print(cmd7)
os.system(cmd7)
# Update class-averages
classavgstack = newclasses
# Import Euler angles
if inangles:
cmd6 = "sxheader.py %s --params=xform.projection --import=%s" % (classavgstack, inangles)
print(cmd6)
header(classavgstack, 'xform.projection', fimport=inangles)
try:
header(classavgstack, 'xform.projection', fexport=outangles)
cmd1 = "sxheader.py %s --params=xform.projection --export=%s" % (classavgstack, outangles)
print(cmd1)
except RuntimeError:
print("\nERROR!! No projection angles found in class-average stack header!\n")
print('Usage:', USAGE)
exit()
#cmd2="sxproject3d.py %s %s --angles=%s" % (recon, projstack, outangles)
#print(cmd2)
#os.system(cmd2)
# Here if you want to be fancy, there should be an option to chose the projection method,
# the mechanism can be copied from sxproject3d.py PAP
if prjmethod=='trilinear':
method_num = 1
elif prjmethod=='gridding':
method_num = -1
elif prjmethod=='nn':
method_num = 0
else:
print("\nERROR!! Valid projection methods are: trilinear (default), gridding, and nn (nearest neighbor).")
print('Usage:', USAGE)
exit()
#project3d(recon, stack=projstack, listagls=outangles)
recon = prep_vol(recon, npad = 2, interpolation_method = 1)
result=[]
# Here you need actual radius to compute proper ccc's, but if you do, you have to deal with translations, PAP
mask = model_circle(nx//2-2,nx,nx)
# Number of images may have changed
nimg1 = EMAN2_cppwrap.EMUtil.get_image_count(classavgstack)
outangles = read_text_row(outangles)
for imgnum in range(nimg1):
# get class average
classimg = get_im(classavgstack, imgnum)
# compute re-projection
prjimg = prgl(recon, outangles[imgnum], 1, False)
# calculate 1D power spectra
rops_dst = rops_table(classimg*mask)
rops_src = rops_table(prjimg)
# Set power spectrum of reprojection to the data.
# Since data has an envelope, it would make more sense to set data to reconstruction,
# but to do it one would have to know the actual resolution of the data.
# you can check sxprocess.py --adjpw to see how this is done properly PAP
table = [0.0]*len(rops_dst) # initialize table
for j in range( len(rops_dst) ):
table[j] = sqrt( old_div(rops_dst[j],rops_src[j]) )
prjimg = fft(filt_table(prjimg, table)) # match FFT amplitdes of re-projection and class average
cccoeff = ccc(prjimg, classimg, mask)
#print(imgnum, cccoeff)
classimg.set_attr_dict({'cross-corr':cccoeff})
prjimg.set_attr_dict({'cross-corr':cccoeff})
prjimg.write_image(outstack,2*imgnum)
classimg.write_image(outstack, 2*imgnum+1)
result.append(cccoeff)
del outangles
meanccc = old_div(sum(result),nimg1)
print("Average CCC is %s" % meanccc)
nimg2 = EMAN2_cppwrap.EMUtil.get_image_count(outstack)
for imgnum in xrange(nimg2):
if (imgnum % 2 ==0):
prjimg = get_im(outstack,imgnum)
meanccc1 = prjimg.get_attr_default('mean-cross-corr', -1.0)
prjimg.set_attr_dict({'mean-cross-corr':meanccc})
write_header(outstack,prjimg,imgnum)
if (imgnum % 100) == 0:
print(imgnum)
# e2proc2d appends to existing files, so delete existing output
if os.path.exists(normstack):
os.remove(normstack)
print("rm %s" % normstack)
# Why would you want to do it? If you do, it should have been done during ccc calculations,
# otherwise what is see is not corresponding to actual data, thus misleading. PAP
#cmd5="e2proc2d.py %s %s --process=normalize" % (outstack, normstack)
#print(cmd5)
#os.system(cmd5)
# Optionally pop up e2display
if displayYN:
cmd8 = "e2display.py %s" % outstack
print(cmd8)
os.system(cmd8)
print("Done!")
def do_volume_mrk02(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if(mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try: local_filter = Tracker["local_filter"]
except: local_filter = False
#=========================================================================
# volume reconstruction
if( type(data) == types.ListType ):
if Tracker["constants"]["CTF"]:
vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if(Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(int(Tracker["constants"]["radius"]*float(nx)/float(Tracker["constants"]["nnxo"])+0.5), nx, nx, nx)
elif(Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if( type(Tracker["constants"]["mask3D"]) == types.StringType ): mask3D = get_im(Tracker["constants"]["mask3D"])
else: mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if( nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(rot_shift3D(mask3D,scale=float(nx)/float(nxm)),nx,nx,nx)
nxm = mask3D.get_xsize()
assert(nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if( Tracker["PWadjustment"] ):
from utilities import read_text_file, write_text_file
rt = read_text_file( Tracker["PWadjustment"] )
fftip(vol)
ro = rops_table(vol)
# Here unless I am mistaken it is enough to take the beginning of the reference pw.
for i in xrange(1,len(ro)): ro[i] = (rt[i]/ro[i])**Tracker["upscale"]
#write_text_file(rops_table(filt_table( vol, ro),1),"foo.txt")
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
from math import exp
for i in xrange(len(ro)): ro[i] *= \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
if local_filter:
# skip low-pass filtration
vol = fft( filt_table( vol, ro) )
else:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = fft( filt_table( filt_table(vol, Tracker["lowpass"]), ro) )
else:
vol = fft( filt_table( filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"]), ro) )
del ro
else:
if Tracker["constants"]["sausage"]:
ny = vol.get_ysize()
y = float(ny)
ro = [0.0]*(ny//2+2)
from math import exp
for i in xrange(len(ro)): ro[i] = \
(1.0+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.10)/0.025)**2)+1.0*exp(-(((i/y/Tracker["constants"]["pixel_size"])-0.215)/0.025)**2))
fftip(vol)
filt_table(vol, ro)
del ro
if not local_filter:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"])
if Tracker["constants"]["sausage"]: vol = fft(vol)
if local_filter:
from morphology import binarize
if(myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node = 0)
# only main processor needs the two input volumes
if(myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if(lx != nx):
if(lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(rot_shift3D(mask,scale=float(lx)/float(nx)),lx,lx,lx)
vol = fdecimate(vol, lx,lx,lx)
else: ERROR("local filter cannot be larger than input volume","user function",1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
else:
lx = 0
locres = model_blank(1,1,1)
vol = model_blank(1,1,1)
lx = bcast_number_to_all(lx, source_node = 0)
if( myid != 0 ): mask = model_blank(lx,lx,lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal( locres, vol, mask, Tracker["falloff"], myid, 0, nproc)
if myid == 0:
if(lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx,nx,nx)
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx,nx,nx)
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
vol = filt_btwl(vol, 0.38, 0.5)# This will have to be corrected.
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def compare(compare_ref_free, outfile_repro, ref_free_output, yrng, xrng,
rstep, nx, apix, ref_free_cutoff, nproc, myid, main_node):
from alignment import Numrinit, ringwe, Applyws
from random import seed, randint
from utilities import get_params2D, set_params2D, model_circle, inverse_transform2, combine_params2
from fundamentals import rot_shift2D
from mpi import MPI_COMM_WORLD, mpi_barrier, mpi_bcast, MPI_INT
from statistics import fsc_mask
from filter import fit_tanh
from numpy import array
fout = "%s.hdf" % ref_free_output
frc_out = "%s_frc" % ref_free_output
res_out = "%s_res" % ref_free_output
nima = EMUtil.get_image_count(compare_ref_free)
image_start, image_end = MPI_start_end(nima, nproc, myid)
ima = EMData()
ima.read_image(compare_ref_free, image_start)
last_ring = nx / 2 - 2
first_ring = 1
mask = model_circle(last_ring, nx, nx)
refi = []
numref = EMUtil.get_image_count(outfile_repro)
cnx = nx / 2 + 1
cny = cnx
mode = "F"
numr = Numrinit(first_ring, last_ring, rstep, mode)
wr = ringwe(numr, mode)
ima.to_zero()
for j in xrange(numref):
temp = EMData()
temp.read_image(outfile_repro, j)
# even, odd, numer of even, number of images. After frc, totav
refi.append(temp)
# for each node read its share of data
data = EMData.read_images(compare_ref_free, range(image_start, image_end))
for im in xrange(image_start, image_end):
data[im - image_start].set_attr('ID', im)
set_params2D(data[im - image_start], [0, 0, 0, 0, 1])
ringref = []
for j in xrange(numref):
refi[j].process_inplace("normalize.mask", {
"mask": mask,
"no_sigma": 1
}) # normalize reference images to N(0,1)
cimage = Util.Polar2Dm(refi[j], cnx, cny, numr, mode)
Util.Frngs(cimage, numr)
Applyws(cimage, numr, wr)
ringref.append(cimage)
if myid == main_node: seed(1000)
data_shift = []
frc = []
res = []
for im in xrange(image_start, image_end):
alpha, sx, sy, mirror, scale = get_params2D(data[im - image_start])
alphai, sxi, syi, scalei = inverse_transform2(alpha, sx, sy, 1.0)
# normalize
data[im - image_start].process_inplace("normalize.mask", {
"mask": mask,
"no_sigma": 1
}) # subtract average under the mask
# align current image to the reference
[angt, sxst, syst, mirrort, xiref,
peakt] = Util.multiref_polar_ali_2d(data[im - image_start], ringref,
xrng, yrng, 1, mode, numr,
cnx + sxi, cny + syi)
iref = int(xiref)
[alphan, sxn, syn, mn] = combine_params2(0.0, -sxi, -syi, 0, angt,
sxst, syst, (int)(mirrort))
set_params2D(data[im - image_start],
[alphan, sxn, syn, int(mn), scale])
temp = rot_shift2D(data[im - image_start], alphan, sxn, syn, mn)
temp.set_attr('assign', iref)
tfrc = fsc_mask(temp, refi[iref], mask=mask)
temp.set_attr('frc', tfrc[1])
res = fit_tanh(tfrc)
temp.set_attr('res', res)
data_shift.append(temp)
for node in xrange(nproc):
if myid == node:
for image in data_shift:
image.write_image(fout, -1)
refindex = image.get_attr('assign')
refi[refindex].write_image(fout, -1)
mpi_barrier(MPI_COMM_WORLD)
rejects = []
if myid == main_node:
a = EMData()
index = 0
frc = []
res = []
temp = []
classes = []
for im in xrange(nima):
a.read_image(fout, index)
frc.append(a.get_attr("frc"))
if ref_free_cutoff != -1:
classes.append(a.get_attr("class_ptcl_idxs"))
tmp = a.get_attr("res")
temp.append(tmp[0])
res.append("%12f" % (apix / tmp[0]))
res.append("\n")
index = index + 2
res_num = array(temp)
mean_score = res_num.mean(axis=0)
std_score = res_num.std(axis=0)
std = std_score / 2
if ref_free_cutoff != -1:
cutoff = mean_score - std * ref_free_cutoff
reject = res_num < cutoff
index = 0
for i in reject:
if i: rejects.extend(classes[index])
index = index + 1
rejects.sort()
length = mpi_bcast(len(rejects), 1, MPI_INT, main_node,
MPI_COMM_WORLD)
rejects = mpi_bcast(rejects, length, MPI_INT, main_node,
MPI_COMM_WORLD)
del a
fout_frc = open(frc_out, 'w')
fout_res = open(res_out, 'w')
fout_res.write("".join(res))
temp = zip(*frc)
datstrings = []
for i in temp:
for j in i:
datstrings.append(" %12f" % (j))
datstrings.append("\n")
fout_frc.write("".join(datstrings))
fout_frc.close()
del refi
del ringref
return rejects
def run3Dalignment(paramsdict, partids, partstack, outputdir, procid, myid, main_node, nproc):
# Reads from paramsdict["stack"] particles partids set parameters in partstack
# and do refinement as specified in paramsdict
#
# Will create outputdir
# Will write to outputdir output parameters: params-chunk0.txt and params-chunk1.txt
if(myid == main_node):
# Create output directory
log = Logger(BaseLogger_Files())
log.prefix = os.path.join(outputdir)
#cmd = "mkdir "+log.prefix
#cmdexecute(cmd)
log.prefix += "/"
else: log = None
mpi_barrier(MPI_COMM_WORLD)
ali3d_options.delta = paramsdict["delta"]
ali3d_options.ts = paramsdict["ts"]
ali3d_options.xr = paramsdict["xr"]
# low pass filter is applied to shrank data, so it has to be adjusted
ali3d_options.fl = paramsdict["lowpass"]/paramsdict["shrink"]
ali3d_options.initfl = paramsdict["initialfl"]/paramsdict["shrink"]
ali3d_options.aa = paramsdict["falloff"]
ali3d_options.maxit = paramsdict["maxit"]
ali3d_options.mask3D = paramsdict["mask3D"]
ali3d_options.an = paramsdict["an"]
ali3d_options.ou = paramsdict["radius"] # This is changed in ali3d_base, but the shrank value is needed in vol recons, fixt it!
shrinkage = paramsdict["shrink"]
projdata = getindexdata(paramsdict["stack"], partids, partstack, myid, nproc)
onx = projdata[0].get_xsize()
last_ring = ali3d_options.ou
if last_ring < 0: last_ring = int(onx/2) - 2
mask2D = model_circle(last_ring,onx,onx) - model_circle(ali3d_options.ir,onx,onx)
if(shrinkage < 1.0):
# get the new size
masks2D = resample(mask2D, shrinkage)
nx = masks2D.get_xsize()
masks2D = model_circle(int(last_ring*shrinkage+0.5),nx,nx) - model_circle(max(int(ali3d_options.ir*shrinkage+0.5),1),nx,nx)
nima = len(projdata)
oldshifts = [0.0,0.0]*nima
for im in xrange(nima):
#data[im].set_attr('ID', list_of_particles[im])
ctf_applied = projdata[im].get_attr_default('ctf_applied', 0)
phi,theta,psi,sx,sy = get_params_proj(projdata[im])
projdata[im] = fshift(projdata[im], sx, sy)
set_params_proj(projdata[im],[phi,theta,psi,0.0,0.0])
# For local SHC set anchor
#if(nsoft == 1 and an[0] > -1):
# set_params_proj(data[im],[phi,tetha,psi,0.0,0.0], "xform.anchor")
oldshifts[im] = [sx,sy]
if ali3d_options.CTF :
ctf_params = projdata[im].get_attr("ctf")
if ctf_applied == 0:
st = Util.infomask(projdata[im], mask2D, False)
projdata[im] -= st[0]
projdata[im] = filt_ctf(projdata[im], ctf_params)
projdata[im].set_attr('ctf_applied', 1)
if(shrinkage < 1.0):
#phi,theta,psi,sx,sy = get_params_proj(projdata[im])
projdata[im] = resample(projdata[im], shrinkage)
st = Util.infomask(projdata[im], None, True)
projdata[im] -= st[0]
st = Util.infomask(projdata[im], masks2D, True)
projdata[im] /= st[1]
#sx *= shrinkage
#sy *= shrinkage
#set_params_proj(projdata[im], [phi,theta,psi,sx,sy])
if ali3d_options.CTF :
ctf_params.apix /= shrinkage
projdata[im].set_attr('ctf', ctf_params)
else:
st = Util.infomask(projdata[im], None, True)
projdata[im] -= st[0]
st = Util.infomask(projdata[im], mask2D, True)
projdata[im] /= st[1]
del mask2D
if(shrinkage < 1.0): del masks2D
"""
if(paramsdict["delpreviousmax"]):
for i in xrange(len(projdata)):
try: projdata[i].del_attr("previousmax")
except: pass
"""
if(myid == main_node):
print_dict(paramsdict,"3D alignment parameters")
print(" => actual lowpass : "******" => actual init lowpass : "******" => PW adjustment : ",ali3d_options.pwreference)
print(" => partids : ",partids)
print(" => partstack : ",partstack)
if(ali3d_options.fl > 0.46): ERROR("Low pass filter in 3D alignment > 0.46 on the scale of shrank data","sxcenter_projections",1,myid)
# Run alignment command, it returns params per CPU
params = center_projections_3D(projdata, paramsdict["refvol"], \
ali3d_options, onx, shrinkage, \
mpi_comm = MPI_COMM_WORLD, myid = myid, main_node = main_node, log = log )
del log, projdata
params = wrap_mpi_gatherv(params, main_node, MPI_COMM_WORLD)
# store params
if(myid == main_node):
for im in xrange(nima):
params[im][0] = params[im][0]/shrinkage +oldshifts[im][0]
params[im][1] = params[im][1]/shrinkage +oldshifts[im][1]
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " =>"
print(line,"Executed successfully: ","3D alignment"," number of images:%7d"%len(params))
write_text_row(params, os.path.join(outputdir,"params.txt") )
def main():
"""
Main function.
Arguments:
None
Returns:
None
"""
command_args = parse_command_line()
# Import volume
print('Import volume.')
input_vol = utilities.get_im(command_args.input_volume)
# Sanity checks
sanity_checks(command_args, input_vol)
try:
os.makedirs(command_args.output_dir)
except OSError:
print('Output directory already exists. No need to create it.')
else:
print('Created output directory.')
output_prefix = os.path.join(command_args.output_dir, command_args.prefix)
# Filter volume if specified
if command_args.low_pass_filter_resolution is not None:
print('Filter volume to {0}A.'.format(
command_args.low_pass_filter_resolution))
input_vol = sparx_filter.filt_tanl(
input_vol,
command_args.pixel_size / command_args.low_pass_filter_resolution,
command_args.low_pass_filter_falloff)
input_vol.write_image(output_prefix + '_filtered_volume.hdf')
else:
print('Skip filter volume.')
# Create a mask based on the filtered volume
print('Create mask')
density_threshold = -9999.0
nsigma = 1.0
if command_args.mol_mass:
density_threshold = input_vol.find_3d_threshold(
command_args.mol_mass, command_args.pixel_size)
elif command_args.threshold:
density_threshold = command_args.threshold
elif command_args.nsigma:
nsigma = command_args.nsigma
else:
assert False
if command_args.edge_type == 'cosine':
mode = 'C'
elif command_args.edge_type == 'gaussian':
mode = 'G'
else:
assert False
mask_first = morphology.adaptive_mask_scipy(
input_vol,
nsigma=nsigma,
threshold=density_threshold,
ndilation=command_args.ndilation,
nerosion=command_args.nerosion,
edge_width=command_args.edge_width,
allow_disconnected=command_args.allow_disconnected,
mode=mode,
do_approx=command_args.do_old,
)
# Create a second mask based on the filtered volume
s_mask = None
s_density_threshold = 1
s_nsigma = 1.0
if command_args.second_mask is not None:
s_mask = utilities.get_im(command_args.second_mask)
density_threshold = -9999.0
nsigma = 1.0
if command_args.s_mol_mass:
s_density_threshold = input_vol.find_3d_threshold(
command_args.s_mol_mass, command_args.s_pixel_size)
elif command_args.s_threshold:
s_density_threshold = command_args.s_threshold
elif command_args.s_nsigma:
s_nsigma = command_args.s_nsigma
else:
assert False
elif command_args.second_mask_shape is not None:
nx = mask_first.get_xsize()
ny = mask_first.get_ysize()
nz = mask_first.get_zsize()
if command_args.second_mask_shape == 'cube':
s_nx = command_args.s_nx
s_ny = command_args.s_ny
s_nz = command_args.s_nz
s_mask = utilities.model_blank(s_nx, s_ny, s_nz, 1)
elif command_args.second_mask_shape == 'cylinder':
s_radius = command_args.s_radius
s_nx = command_args.s_nx
s_ny = command_args.s_ny
s_nz = command_args.s_nz
s_mask = utilities.model_cylinder(s_radius, s_nx, s_ny, s_nz)
elif command_args.second_mask_shape == 'sphere':
s_radius = command_args.s_radius
s_nx = command_args.s_nx
s_ny = command_args.s_ny
s_nz = command_args.s_nz
s_mask = utilities.model_circle(s_radius, s_nx, s_ny, s_nz)
else:
assert False
s_mask = utilities.pad(s_mask, nx, ny, nz, 0)
if s_mask is not None:
print('Create second mask')
if command_args.s_edge_type == 'cosine':
mode = 'C'
elif command_args.s_edge_type == 'gaussian':
mode = 'G'
else:
assert False
s_mask = morphology.adaptive_mask_scipy(
s_mask,
nsigma=s_nsigma,
threshold=s_density_threshold,
ndilation=command_args.s_ndilation,
nerosion=command_args.s_nerosion,
edge_width=command_args.s_edge_width,
allow_disconnected=command_args.s_allow_disconnected,
mode=mode,
do_approx=command_args.s_do_old)
if command_args.s_invert:
s_mask = 1 - s_mask
mask_first.write_image(output_prefix + '_mask_first.hdf')
s_mask.write_image(output_prefix + '_mask_second.hdf')
masked_combined = mask_first * s_mask
masked_combined.write_image(output_prefix + '_mask.hdf')
else:
mask_first.write_image(output_prefix + '_mask.hdf')
def do_volume_mrk03(ref_data):
"""
data - projections (scattered between cpus) or the volume. If volume, just do the volume processing
options - the same for all cpus
return - volume the same for all cpus
"""
from EMAN2 import Util
from mpi import mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from filter import filt_table
from reconstruction import recons3d_4nn_MPI, recons3d_4nnw_MPI # recons3d_4nn_ctf_MPI
from utilities import bcast_EMData_to_all, bcast_number_to_all, model_blank
from fundamentals import rops_table, fftip, fft
import types
# Retrieve the function specific input arguments from ref_data
data = ref_data[0]
Tracker = ref_data[1]
iter = ref_data[2]
mpi_comm = ref_data[3]
# # For DEBUG
# print "Type of data %s" % (type(data))
# print "Type of Tracker %s" % (type(Tracker))
# print "Type of iter %s" % (type(iter))
# print "Type of mpi_comm %s" % (type(mpi_comm))
if(mpi_comm == None): mpi_comm = MPI_COMM_WORLD
myid = mpi_comm_rank(mpi_comm)
nproc = mpi_comm_size(mpi_comm)
try: local_filter = Tracker["local_filter"]
except: local_filter = False
#=========================================================================
# volume reconstruction
if( type(data) == types.ListType ):
if Tracker["constants"]["CTF"]:
#vol = recons3d_4nn_ctf_MPI(myid, data, Tracker["constants"]["snr"], \
# symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
vol = recons3d_4nnw_MPI(myid, data, Tracker["bckgnoise"], Tracker["constants"]["snr"], \
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm, smearstep = Tracker["smearstep"])
else:
vol = recons3d_4nn_MPI (myid, data,\
symmetry=Tracker["constants"]["sym"], npad=Tracker["constants"]["npad"], mpi_comm=mpi_comm)
else:
vol = data
if myid == 0:
from morphology import threshold
from filter import filt_tanl, filt_btwl
from utilities import model_circle, get_im
import types
nx = vol.get_xsize()
if(Tracker["constants"]["mask3D"] == None):
mask3D = model_circle(int(Tracker["constants"]["radius"]*float(nx)/float(Tracker["constants"]["nnxo"])+0.5), nx, nx, nx)
elif(Tracker["constants"]["mask3D"] == "auto"):
from utilities import adaptive_mask
mask3D = adaptive_mask(vol)
else:
if( type(Tracker["constants"]["mask3D"]) == types.StringType ): mask3D = get_im(Tracker["constants"]["mask3D"])
else: mask3D = (Tracker["constants"]["mask3D"]).copy()
nxm = mask3D.get_xsize()
if( nx != nxm):
from fundamentals import rot_shift3D
mask3D = Util.window(rot_shift3D(mask3D,scale=float(nx)/float(nxm)),nx,nx,nx)
nxm = mask3D.get_xsize()
assert(nx == nxm)
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
if not local_filter:
if( type(Tracker["lowpass"]) == types.ListType ):
vol = filt_table(vol, Tracker["lowpass"])
else:
vol = filt_tanl(vol, Tracker["lowpass"], Tracker["falloff"])
if local_filter:
from morphology import binarize
if(myid == 0): nx = mask3D.get_xsize()
else: nx = 0
nx = bcast_number_to_all(nx, source_node = 0)
# only main processor needs the two input volumes
if(myid == 0):
mask = binarize(mask3D, 0.5)
locres = get_im(Tracker["local_filter"])
lx = locres.get_xsize()
if(lx != nx):
if(lx < nx):
from fundamentals import fdecimate, rot_shift3D
mask = Util.window(rot_shift3D(mask,scale=float(lx)/float(nx)),lx,lx,lx)
vol = fdecimate(vol, lx,lx,lx)
else: ERROR("local filter cannot be larger than input volume","user function",1)
stat = Util.infomask(vol, mask, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
else:
lx = 0
locres = model_blank(1,1,1)
vol = model_blank(1,1,1)
lx = bcast_number_to_all(lx, source_node = 0)
if( myid != 0 ): mask = model_blank(lx,lx,lx)
bcast_EMData_to_all(mask, myid, 0, comm=mpi_comm)
from filter import filterlocal
vol = filterlocal( locres, vol, mask, Tracker["falloff"], myid, 0, nproc)
if myid == 0:
if(lx < nx):
from fundamentals import fpol
vol = fpol(vol, nx,nx,nx)
vol = threshold(vol)
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
else:
vol = model_blank(nx,nx,nx)
"""
else:
if myid == 0:
#from utilities import write_text_file
#write_text_file(rops_table(vol,1),"goo.txt")
stat = Util.infomask(vol, mask3D, False)
vol -= stat[0]
Util.mul_scalar(vol, 1.0/stat[1])
vol = threshold(vol)
Util.mul_img(vol, mask3D)
del mask3D
# vol.write_image('toto%03d.hdf'%iter)
"""
# broadcast volume
bcast_EMData_to_all(vol, myid, 0, comm=mpi_comm)
#=========================================================================
return vol
def calculate_volumes_after_rotation_and_save_them(ali3d_options, rviper_iter, masterdir, bdb_stack_location, mpi_rank, mpi_size,
no_of_viper_runs_analyzed_together, no_of_viper_runs_analyzed_together_from_user_options, mpi_comm = -1):
# This function takes into account the case in which there are more processors than images
if mpi_comm == -1:
mpi_comm = MPI_COMM_WORLD
# some arguments are for debugging purposes
mainoutputdir = masterdir + DIR_DELIM + NAME_OF_MAIN_DIR + ("%03d" + DIR_DELIM) %(rviper_iter)
# list_of_projection_indices_used_for_outlier_elimination = map(int, read_text_file(mainoutputdir + DIR_DELIM + "list_of_viper_runs_included_in_outlier_elimination.txt"))
import json; f = open(mainoutputdir + "list_of_viper_runs_included_in_outlier_elimination.json", 'r')
list_of_independent_viper_run_indices_used_for_outlier_elimination = json.load(f); f.close()
if len(list_of_independent_viper_run_indices_used_for_outlier_elimination)==0:
print "Error: len(list_of_independent_viper_run_indices_used_for_outlier_elimination)==0"
mpi_finalize()
sys.exit()
# if this data analysis step was already performed in the past then return
# for future changes make sure that the file checked is the last one to be processed !!!
# if(os.path.exists(mainoutputdir + DIR_DELIM + NAME_OF_RUN_DIR + "%03d"%(no_of_viper_runs_analyzed_together - 1) + DIR_DELIM + "rotated_volume.hdf")):
# check_last_run = max(get_latest_directory_increment_value(mainoutputdir, NAME_OF_RUN_DIR, start_value=0), no_of_viper_runs_analyzed_together_from_user_options)
# if(os.path.exists(mainoutputdir + DIR_DELIM + NAME_OF_RUN_DIR + "%03d"%(check_last_run) + DIR_DELIM + "rotated_volume.hdf")):
# return
# if this data analysis step was already performed in the past then return
for check_run in list_of_independent_viper_run_indices_used_for_outlier_elimination:
if not (os.path.exists(mainoutputdir + DIR_DELIM + NAME_OF_RUN_DIR + "%03d"%(check_run) + DIR_DELIM + "rotated_volume.hdf")):
break
else:
return
partstack = []
# for i1 in range(0,no_of_viper_runs_analyzed_together):
for i1 in list_of_independent_viper_run_indices_used_for_outlier_elimination:
partstack.append(mainoutputdir + NAME_OF_RUN_DIR + "%03d"%(i1) + DIR_DELIM + "rotated_reduced_params.txt")
partids_file_name = mainoutputdir + "this_iteration_index_keep_images.txt"
lpartids = map(int, read_text_file(partids_file_name) )
n_projs = len(lpartids)
if (mpi_size > n_projs):
# if there are more processors than images
working = int(not(mpi_rank < n_projs))
mpi_subcomm = mpi_comm_split(mpi_comm, working, mpi_rank - working*n_projs)
mpi_subsize = mpi_comm_size(mpi_subcomm)
mpi_subrank = mpi_comm_rank(mpi_subcomm)
if (mpi_rank < n_projs):
# for i in xrange(no_of_viper_runs_analyzed_together):
for idx, i in enumerate(list_of_independent_viper_run_indices_used_for_outlier_elimination):
projdata = getindexdata(bdb_stack_location + "_%03d"%(rviper_iter - 1), partids_file_name, partstack[idx], mpi_rank, mpi_subsize)
vol = do_volume(projdata, ali3d_options, 0, mpi_comm = mpi_subcomm)
del projdata
if( mpi_rank == 0):
vol.write_image(mainoutputdir + DIR_DELIM + NAME_OF_RUN_DIR + "%03d"%(i) + DIR_DELIM + "rotated_volume.hdf")
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " => "
print line + "Generated rec_ref_volume_run #%01d \n"%i
del vol
mpi_barrier(mpi_comm)
else:
for idx, i in enumerate(list_of_independent_viper_run_indices_used_for_outlier_elimination):
projdata = getindexdata(bdb_stack_location + "_%03d"%(rviper_iter - 1), partids_file_name, partstack[idx], mpi_rank, mpi_size)
vol = do_volume(projdata, ali3d_options, 0, mpi_comm = mpi_comm)
del projdata
if( mpi_rank == 0):
vol.write_image(mainoutputdir + DIR_DELIM + NAME_OF_RUN_DIR + "%03d"%(i) + DIR_DELIM + "rotated_volume.hdf")
line = strftime("%Y-%m-%d_%H:%M:%S", localtime()) + " => "
print line + "Generated rec_ref_volume_run #%01d"%i
del vol
if( mpi_rank == 0):
# Align all rotated volumes, calculate their average and save as an overall result
from utilities import get_params3D, set_params3D, get_im, model_circle
from statistics import ave_var
from applications import ali_vol
# vls = [None]*no_of_viper_runs_analyzed_together
vls = [None]*len(list_of_independent_viper_run_indices_used_for_outlier_elimination)
# for i in xrange(no_of_viper_runs_analyzed_together):
for idx, i in enumerate(list_of_independent_viper_run_indices_used_for_outlier_elimination):
vls[idx] = get_im(mainoutputdir + DIR_DELIM + NAME_OF_RUN_DIR + "%03d"%(i) + DIR_DELIM + "rotated_volume.hdf")
set_params3D(vls[idx],[0.,0.,0.,0.,0.,0.,0,1.0])
asa,sas = ave_var(vls)
# do the alignment
nx = asa.get_xsize()
radius = nx/2 - .5
st = Util.infomask(asa*asa, model_circle(radius,nx,nx,nx), True)
goal = st[0]
going = True
while(going):
set_params3D(asa,[0.,0.,0.,0.,0.,0.,0,1.0])
# for i in xrange(no_of_viper_runs_analyzed_together):
for idx, i in enumerate(list_of_independent_viper_run_indices_used_for_outlier_elimination):
o = ali_vol(vls[idx],asa,7.0,5.,radius) # range of angles and shifts, maybe should be adjusted
p = get_params3D(o)
del o
set_params3D(vls[idx],p)
asa,sas = ave_var(vls)
st = Util.infomask(asa*asa, model_circle(radius,nx,nx,nx), True)
if(st[0] > goal): goal = st[0]
else: going = False
# over and out
asa.write_image(mainoutputdir + DIR_DELIM + "average_volume.hdf")
sas.write_image(mainoutputdir + DIR_DELIM + "variance_volume.hdf")
return
def ali3d_MPI(stack, ref_vol, outdir, maskfile = None, ir = 1, ou = -1, rs = 1,
xr = "4 2 2 1", yr = "-1", ts = "1 1 0.5 0.25", delta = "10 6 4 4", an = "-1",
center = 0, maxit = 5, term = 95, CTF = False, fourvar = False, snr = 1.0, ref_a = "S", sym = "c1",
sort=True, cutoff=999.99, pix_cutoff="0", two_tail=False, model_jump="1 1 1 1 1", restart=False, save_half=False,
protos=None, oplane=None, lmask=-1, ilmask=-1, findseam=False, vertstep=None, hpars="-1", hsearch="73.0 170.0",
full_output = False, compare_repro = False, compare_ref_free = "-1", ref_free_cutoff= "-1 -1 -1 -1",
wcmask = None, debug = False, recon_pad = 4):
from alignment import Numrinit, prepare_refrings
from utilities import model_circle, get_image, drop_image, get_input_from_string
from utilities import bcast_list_to_all, bcast_number_to_all, reduce_EMData_to_root, bcast_EMData_to_all
from utilities import send_attr_dict
from utilities import get_params_proj, file_type
from fundamentals import rot_avg_image
import os
import types
from utilities import print_begin_msg, print_end_msg, print_msg
from mpi import mpi_bcast, mpi_comm_size, mpi_comm_rank, MPI_FLOAT, MPI_COMM_WORLD, mpi_barrier, mpi_reduce
from mpi import mpi_reduce, MPI_INT, MPI_SUM, mpi_finalize
from filter import filt_ctf
from projection import prep_vol, prgs
from statistics import hist_list, varf3d_MPI, fsc_mask
from numpy import array, bincount, array2string, ones
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
if myid == main_node:
if os.path.exists(outdir): ERROR('Output directory exists, please change the name and restart the program', "ali3d_MPI", 1)
os.mkdir(outdir)
mpi_barrier(MPI_COMM_WORLD)
if debug:
from time import sleep
while not os.path.exists(outdir):
print "Node ",myid," waiting..."
sleep(5)
info_file = os.path.join(outdir, "progress%04d"%myid)
finfo = open(info_file, 'w')
else:
finfo = None
mjump = get_input_from_string(model_jump)
xrng = get_input_from_string(xr)
if yr == "-1": yrng = xrng
else : yrng = get_input_from_string(yr)
step = get_input_from_string(ts)
delta = get_input_from_string(delta)
ref_free_cutoff = get_input_from_string(ref_free_cutoff)
pix_cutoff = get_input_from_string(pix_cutoff)
lstp = min(len(xrng), len(yrng), len(step), len(delta))
if an == "-1":
an = [-1] * lstp
else:
an = get_input_from_string(an)
# make sure pix_cutoff is set for all iterations
if len(pix_cutoff)<lstp:
for i in xrange(len(pix_cutoff),lstp):
pix_cutoff.append(pix_cutoff[-1])
# don't waste time on sub-pixel alignment for low-resolution ang incr
for i in range(len(step)):
if (delta[i] > 4 or delta[i] == -1) and step[i] < 1:
step[i] = 1
first_ring = int(ir)
rstep = int(rs)
last_ring = int(ou)
max_iter = int(maxit)
center = int(center)
nrefs = EMUtil.get_image_count( ref_vol )
nmasks = 0
if maskfile:
# read number of masks within each maskfile (mc)
nmasks = EMUtil.get_image_count( maskfile )
# open masks within maskfile (mc)
maskF = EMData.read_images(maskfile, xrange(nmasks))
vol = EMData.read_images(ref_vol, xrange(nrefs))
nx = vol[0].get_xsize()
## make sure box sizes are the same
if myid == main_node:
im=EMData.read_images(stack,[0])
bx = im[0].get_xsize()
if bx!=nx:
print_msg("Error: Stack box size (%i) differs from initial model (%i)\n"%(bx,nx))
sys.exit()
del im,bx
# for helical processing:
helicalrecon = False
if protos is not None or hpars != "-1" or findseam is True:
helicalrecon = True
# if no out-of-plane param set, use 5 degrees
if oplane is None:
oplane=5.0
if protos is not None:
proto = get_input_from_string(protos)
if len(proto) != nrefs:
print_msg("Error: insufficient protofilament numbers supplied")
sys.exit()
if hpars != "-1":
hpars = get_input_from_string(hpars)
if len(hpars) != 2*nrefs:
print_msg("Error: insufficient helical parameters supplied")
sys.exit()
## create helical parameter file for helical reconstruction
if helicalrecon is True and myid == main_node:
from hfunctions import createHpar
# create initial helical parameter files
dp=[0]*nrefs
dphi=[0]*nrefs
vdp=[0]*nrefs
vdphi=[0]*nrefs
for iref in xrange(nrefs):
hpar = os.path.join(outdir,"hpar%02d.spi"%(iref))
params = False
if hpars != "-1":
# if helical parameters explicitly given, set twist & rise
params = [float(hpars[iref*2]),float(hpars[(iref*2)+1])]
dp[iref],dphi[iref],vdp[iref],vdphi[iref] = createHpar(hpar,proto[iref],params,vertstep)
# get values for helical search parameters
hsearch = get_input_from_string(hsearch)
if len(hsearch) != 2:
print_msg("Error: specify outer and inner radii for helical search")
sys.exit()
if last_ring < 0 or last_ring > int(nx/2)-2 : last_ring = int(nx/2) - 2
if myid == main_node:
# import user_functions
# user_func = user_functions.factory[user_func_name]
print_begin_msg("ali3d_MPI")
print_msg("Input stack : %s\n"%(stack))
print_msg("Reference volume : %s\n"%(ref_vol))
print_msg("Output directory : %s\n"%(outdir))
if nmasks > 0:
print_msg("Maskfile (number of masks) : %s (%i)\n"%(maskfile,nmasks))
print_msg("Inner radius : %i\n"%(first_ring))
print_msg("Outer radius : %i\n"%(last_ring))
print_msg("Ring step : %i\n"%(rstep))
print_msg("X search range : %s\n"%(xrng))
print_msg("Y search range : %s\n"%(yrng))
print_msg("Translational step : %s\n"%(step))
print_msg("Angular step : %s\n"%(delta))
print_msg("Angular search range : %s\n"%(an))
print_msg("Maximum iteration : %i\n"%(max_iter))
print_msg("Center type : %i\n"%(center))
print_msg("CTF correction : %s\n"%(CTF))
print_msg("Signal-to-Noise Ratio : %f\n"%(snr))
print_msg("Reference projection method : %s\n"%(ref_a))
print_msg("Symmetry group : %s\n"%(sym))
print_msg("Fourier padding for 3D : %i\n"%(recon_pad))
print_msg("Number of reference models : %i\n"%(nrefs))
print_msg("Sort images between models : %s\n"%(sort))
print_msg("Allow images to jump : %s\n"%(mjump))
print_msg("CC cutoff standard dev : %f\n"%(cutoff))
print_msg("Two tail cutoff : %s\n"%(two_tail))
print_msg("Termination pix error : %f\n"%(term))
print_msg("Pixel error cutoff : %s\n"%(pix_cutoff))
print_msg("Restart : %s\n"%(restart))
print_msg("Full output : %s\n"%(full_output))
print_msg("Compare reprojections : %s\n"%(compare_repro))
print_msg("Compare ref free class avgs : %s\n"%(compare_ref_free))
print_msg("Use cutoff from ref free : %s\n"%(ref_free_cutoff))
if protos:
print_msg("Protofilament numbers : %s\n"%(proto))
print_msg("Using helical search range : %s\n"%hsearch)
if findseam is True:
print_msg("Using seam-based reconstruction\n")
if hpars != "-1":
print_msg("Using hpars : %s\n"%hpars)
if vertstep != None:
print_msg("Using vertical step : %.2f\n"%vertstep)
if save_half is True:
print_msg("Saving even/odd halves\n")
for i in xrange(100) : print_msg("*")
print_msg("\n\n")
if maskfile:
if type(maskfile) is types.StringType: mask3D = get_image(maskfile)
else: mask3D = maskfile
else: mask3D = model_circle(last_ring, nx, nx, nx)
numr = Numrinit(first_ring, last_ring, rstep, "F")
mask2D = model_circle(last_ring,nx,nx) - model_circle(first_ring,nx,nx)
fscmask = model_circle(last_ring,nx,nx,nx)
if CTF:
from filter import filt_ctf
from reconstruction_rjh import rec3D_MPI_noCTF
if myid == main_node:
active = EMUtil.get_all_attributes(stack, 'active')
list_of_particles = []
for im in xrange(len(active)):
if active[im]: list_of_particles.append(im)
del active
nima = len(list_of_particles)
else:
nima = 0
total_nima = bcast_number_to_all(nima, source_node = main_node)
if myid != main_node:
list_of_particles = [-1]*total_nima
list_of_particles = bcast_list_to_all(list_of_particles, source_node = main_node)
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
# create a list of images for each node
list_of_particles = list_of_particles[image_start: image_end]
nima = len(list_of_particles)
if debug:
finfo.write("image_start, image_end: %d %d\n" %(image_start, image_end))
finfo.flush()
data = EMData.read_images(stack, list_of_particles)
t_zero = Transform({"type":"spider","phi":0,"theta":0,"psi":0,"tx":0,"ty":0})
transmulti = [[t_zero for i in xrange(nrefs)] for j in xrange(nima)]
for iref,im in ((iref,im) for iref in xrange(nrefs) for im in xrange(nima)):
if nrefs == 1:
transmulti[im][iref] = data[im].get_attr("xform.projection")
else:
# if multi models, keep track of eulers for all models
try:
transmulti[im][iref] = data[im].get_attr("eulers_txty.%i"%iref)
except:
data[im].set_attr("eulers_txty.%i"%iref,t_zero)
scoremulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
pixelmulti = [[0.0 for i in xrange(nrefs)] for j in xrange(nima)]
ref_res = [0.0 for x in xrange(nrefs)]
apix = data[0].get_attr('apix_x')
# for oplane parameter, create cylindrical mask
if oplane is not None and myid == main_node:
from hfunctions import createCylMask
cmaskf=os.path.join(outdir, "mask3D_cyl.mrc")
mask3D = createCylMask(data,ou,lmask,ilmask,cmaskf)
# if finding seam of helix, create wedge masks
if findseam is True:
wedgemask=[]
for pf in xrange(nrefs):
wedgemask.append(EMData())
# wedgemask option
if wcmask is not None:
wcmask = get_input_from_string(wcmask)
if len(wcmask) != 3:
print_msg("Error: wcmask option requires 3 values: x y radius")
sys.exit()
# determine if particles have helix info:
try:
data[0].get_attr('h_angle')
original_data = []
boxmask = True
from hfunctions import createBoxMask
except:
boxmask = False
# prepare particles
for im in xrange(nima):
data[im].set_attr('ID', list_of_particles[im])
data[im].set_attr('pix_score', int(0))
if CTF:
# only phaseflip particles, not full CTF correction
ctf_params = data[im].get_attr("ctf")
st = Util.infomask(data[im], mask2D, False)
data[im] -= st[0]
data[im] = filt_ctf(data[im], ctf_params, sign = -1, binary=1)
data[im].set_attr('ctf_applied', 1)
# for window mask:
if boxmask is True:
h_angle = data[im].get_attr("h_angle")
original_data.append(data[im].copy())
bmask = createBoxMask(nx,apix,ou,lmask,h_angle)
data[im]*=bmask
del bmask
if debug:
finfo.write( '%d loaded \n' % nima )
finfo.flush()
if myid == main_node:
# initialize data for the reference preparation function
ref_data = [ mask3D, max(center,0), None, None, None, None ]
# for method -1, switch off centering in user function
from time import time
# this is needed for gathering of pixel errors
disps = []
recvcount = []
disps_score = []
recvcount_score = []
for im in xrange(number_of_proc):
if( im == main_node ):
disps.append(0)
disps_score.append(0)
else:
disps.append(disps[im-1] + recvcount[im-1])
disps_score.append(disps_score[im-1] + recvcount_score[im-1])
ib, ie = MPI_start_end(total_nima, number_of_proc, im)
recvcount.append( ie - ib )
recvcount_score.append((ie-ib)*nrefs)
pixer = [0.0]*nima
cs = [0.0]*3
total_iter = 0
volodd = EMData.read_images(ref_vol, xrange(nrefs))
voleve = EMData.read_images(ref_vol, xrange(nrefs))
if restart:
# recreate initial volumes from alignments stored in header
itout = "000_00"
for iref in xrange(nrefs):
if(nrefs == 1):
modout = ""
else:
modout = "_model_%02d"%(iref)
if(sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection',transmulti[im][iref])
fscfile = os.path.join(outdir, "fsc_%s%s"%(itout,modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(data, sym, fscmask, fscfile, myid, main_node, index = group, npad = recon_pad)
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep=None
if vertstep is not None:
vstep=(vdp[iref],vdphi[iref])
print_msg("Old rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
hvals=processHelicalVol(vol[iref],voleve[iref],volodd[iref],iref,outdir,itout,
dp[iref],dphi[iref],apix,hsearch,findseam,vstep,wcmask)
(vol[iref],voleve[iref],volodd[iref],dp[iref],dphi[iref],vdp[iref],vdphi[iref])=hvals
print_msg("New rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask( volodd[iref], voleve[iref], mask3D, rstep, fscfile)
else:
vol[iref].write_image(os.path.join(outdir, "vol_%s.hdf"%itout),-1)
if save_half is True:
volodd[iref].write_image(os.path.join(outdir, "volodd_%s.hdf"%itout),-1)
voleve[iref].write_image(os.path.join(outdir, "voleve_%s.hdf"%itout),-1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs,fl = ref_ali3d(ref_data)
vol[iref].write_image(os.path.join(outdir, "volf_%s.hdf"%(itout)),-1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res = fl
else:
res_msg = "Models filtered at resolution of: \t"
res = apix / fl
ares = array2string(array(res), precision = 2)
print_msg("%s%s\n\n"%(res_msg,ares))
bcast_EMData_to_all(vol[iref], myid, main_node)
# write out headers, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
# projection matching
for N_step in xrange(lstp):
terminate = 0
Iter = -1
while(Iter < max_iter-1 and terminate == 0):
Iter += 1
total_iter += 1
itout = "%03g_%02d" %(delta[N_step], Iter)
if myid == main_node:
print_msg("ITERATION #%3d, inner iteration #%3d\nDelta = %4.1f, an = %5.2f, xrange = %5.2f, yrange = %5.2f, step = %5.2f\n\n"%(N_step, Iter, delta[N_step], an[N_step], xrng[N_step],yrng[N_step],step[N_step]))
for iref in xrange(nrefs):
if myid == main_node: start_time = time()
volft,kb = prep_vol( vol[iref] )
## constrain projections to out of plane parameter
theta1 = None
theta2 = None
if oplane is not None:
theta1 = 90-oplane
theta2 = 90+oplane
refrings = prepare_refrings( volft, kb, nx, delta[N_step], ref_a, sym, numr, MPI=True, phiEqpsi = "Minus", initial_theta=theta1, delta_theta=theta2)
del volft,kb
if myid== main_node:
print_msg( "Time to prepare projections for model %i: %s\n" % (iref, legibleTime(time()-start_time)) )
start_time = time()
for im in xrange( nima ):
data[im].set_attr("xform.projection", transmulti[im][iref])
if an[N_step] == -1:
t1, peak, pixer[im] = proj_ali_incore(data[im],refrings,numr,xrng[N_step],yrng[N_step],step[N_step],finfo)
else:
t1, peak, pixer[im] = proj_ali_incore_local(data[im],refrings,numr,xrng[N_step],yrng[N_step],step[N_step],an[N_step],finfo)
#data[im].set_attr("xform.projection"%iref, t1)
if nrefs > 1: data[im].set_attr("eulers_txty.%i"%iref,t1)
scoremulti[im][iref] = peak
from pixel_error import max_3D_pixel_error
# t1 is the current param, t2 is old
t2 = transmulti[im][iref]
pixelmulti[im][iref] = max_3D_pixel_error(t1,t2,numr[-3])
transmulti[im][iref] = t1
if myid == main_node:
print_msg("Time of alignment for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
# gather scoring data from all processors
from mpi import mpi_gatherv
scoremultisend = sum(scoremulti,[])
pixelmultisend = sum(pixelmulti,[])
tmp = mpi_gatherv(scoremultisend,len(scoremultisend),MPI_FLOAT, recvcount_score, disps_score, MPI_FLOAT, main_node,MPI_COMM_WORLD)
tmp1 = mpi_gatherv(pixelmultisend,len(pixelmultisend),MPI_FLOAT, recvcount_score, disps_score, MPI_FLOAT, main_node,MPI_COMM_WORLD)
tmp = mpi_bcast(tmp,(total_nima * nrefs), MPI_FLOAT,0, MPI_COMM_WORLD)
tmp1 = mpi_bcast(tmp1,(total_nima * nrefs), MPI_FLOAT,0, MPI_COMM_WORLD)
tmp = map(float,tmp)
tmp1 = map(float,tmp1)
score = array(tmp).reshape(-1,nrefs)
pixelerror = array(tmp1).reshape(-1,nrefs)
score_local = array(scoremulti)
mean_score = score.mean(axis=0)
std_score = score.std(axis=0)
cut = mean_score - (cutoff * std_score)
cut2 = mean_score + (cutoff * std_score)
res_max = score_local.argmax(axis=1)
minus_cc = [0.0 for x in xrange(nrefs)]
minus_pix = [0.0 for x in xrange(nrefs)]
minus_ref = [0.0 for x in xrange(nrefs)]
#output pixel errors
if(myid == main_node):
from statistics import hist_list
lhist = 20
pixmin = pixelerror.min(axis=1)
region, histo = hist_list(pixmin, lhist)
if(region[0] < 0.0): region[0] = 0.0
print_msg("Histogram of pixel errors\n ERROR number of particles\n")
for lhx in xrange(lhist):
print_msg(" %10.3f %7d\n"%(region[lhx], histo[lhx]))
# Terminate if 95% within 1 pixel error
im = 0
for lhx in xrange(lhist):
if(region[lhx] > 1.0): break
im += histo[lhx]
print_msg( "Percent of particles with pixel error < 1: %f\n\n"% (im/float(total_nima)*100))
term_cond = float(term)/100
if(im/float(total_nima) > term_cond):
terminate = 1
print_msg("Terminating internal loop\n")
del region, histo
terminate = mpi_bcast(terminate, 1, MPI_INT, 0, MPI_COMM_WORLD)
terminate = int(terminate[0])
for im in xrange(nima):
if(sort==False):
data[im].set_attr('group',999)
elif (mjump[N_step]==1):
data[im].set_attr('group',int(res_max[im]))
pix_run = data[im].get_attr('pix_score')
if (pix_cutoff[N_step]==1 and (terminate==1 or Iter == max_iter-1)):
if (pixelmulti[im][int(res_max[im])] > 1):
data[im].set_attr('pix_score',int(777))
if (score_local[im][int(res_max[im])]<cut[int(res_max[im])]) or (two_tail and score_local[im][int(res_max[im])]>cut2[int(res_max[im])]):
data[im].set_attr('group',int(888))
minus_cc[int(res_max[im])] = minus_cc[int(res_max[im])] + 1
if(pix_run == 777):
data[im].set_attr('group',int(777))
minus_pix[int(res_max[im])] = minus_pix[int(res_max[im])] + 1
if (compare_ref_free != "-1") and (ref_free_cutoff[N_step] != -1) and (total_iter > 1):
id = data[im].get_attr('ID')
if id in rejects:
data[im].set_attr('group',int(666))
minus_ref[int(res_max[im])] = minus_ref[int(res_max[im])] + 1
minus_cc_tot = mpi_reduce(minus_cc,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
minus_pix_tot = mpi_reduce(minus_pix,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
minus_ref_tot = mpi_reduce(minus_ref,nrefs,MPI_FLOAT,MPI_SUM,0,MPI_COMM_WORLD)
if (myid == main_node):
if(sort):
tot_max = score.argmax(axis=1)
res = bincount(tot_max)
else:
res = ones(nrefs) * total_nima
print_msg("Particle distribution: \t\t%s\n"%(res*1.0))
afcut1 = res - minus_cc_tot
afcut2 = afcut1 - minus_pix_tot
afcut3 = afcut2 - minus_ref_tot
print_msg("Particle distribution after cc cutoff:\t\t%s\n"%(afcut1))
print_msg("Particle distribution after pix cutoff:\t\t%s\n"%(afcut2))
print_msg("Particle distribution after ref cutoff:\t\t%s\n\n"%(afcut3))
res = [0.0 for i in xrange(nrefs)]
for iref in xrange(nrefs):
if(center == -1):
from utilities import estimate_3D_center_MPI, rotate_3D_shift
dummy=EMData()
cs[0], cs[1], cs[2], dummy, dummy = estimate_3D_center_MPI(data, total_nima, myid, number_of_proc, main_node)
cs = mpi_bcast(cs, 3, MPI_FLOAT, main_node, MPI_COMM_WORLD)
cs = [-float(cs[0]), -float(cs[1]), -float(cs[2])]
rotate_3D_shift(data, cs)
if(sort):
group = iref
for im in xrange(nima):
imgroup = data[im].get_attr('group')
if imgroup == iref:
data[im].set_attr('xform.projection',transmulti[im][iref])
else:
group = int(999)
for im in xrange(nima):
data[im].set_attr('xform.projection',transmulti[im][iref])
if(nrefs == 1):
modout = ""
else:
modout = "_model_%02d"%(iref)
fscfile = os.path.join(outdir, "fsc_%s%s"%(itout,modout))
vol[iref], fscc, volodd[iref], voleve[iref] = rec3D_MPI_noCTF(data, sym, fscmask, fscfile, myid, main_node, index=group, npad=recon_pad)
if myid == main_node:
print_msg("3D reconstruction time for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
# Compute Fourier variance
if fourvar:
outvar = os.path.join(outdir, "volVar_%s.hdf"%(itout))
ssnr_file = os.path.join(outdir, "ssnr_%s"%(itout))
varf = varf3d_MPI(data, ssnr_text_file=ssnr_file, mask2D=None, reference_structure=vol[iref], ou=last_ring, rw=1.0, npad=1, CTF=None, sign=1, sym=sym, myid=myid)
if myid == main_node:
print_msg("Time to calculate 3D Fourier variance for model %i: %s\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
varf = 1.0/varf
varf.write_image(outvar,-1)
else: varf = None
if myid == main_node:
if helicalrecon:
from hfunctions import processHelicalVol
vstep=None
if vertstep is not None:
vstep=(vdp[iref],vdphi[iref])
print_msg("Old rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
hvals=processHelicalVol(vol[iref],voleve[iref],volodd[iref],iref,outdir,itout,
dp[iref],dphi[iref],apix,hsearch,findseam,vstep,wcmask)
(vol[iref],voleve[iref],volodd[iref],dp[iref],dphi[iref],vdp[iref],vdphi[iref])=hvals
print_msg("New rise and twist for model %i : %8.3f, %8.3f\n"%(iref,dp[iref],dphi[iref]))
# get new FSC from symmetrized half volumes
fscc = fsc_mask( volodd[iref], voleve[iref], mask3D, rstep, fscfile)
print_msg("Time to search and apply helical symmetry for model %i: %s\n\n"%(iref, legibleTime(time()-start_time)))
start_time = time()
else:
vol[iref].write_image(os.path.join(outdir, "vol_%s.hdf"%(itout)),-1)
if save_half is True:
volodd[iref].write_image(os.path.join(outdir, "volodd_%s.hdf"%(itout)),-1)
voleve[iref].write_image(os.path.join(outdir, "voleve_%s.hdf"%(itout)),-1)
if nmasks > 1:
# Read mask for multiplying
ref_data[0] = maskF[iref]
ref_data[2] = vol[iref]
ref_data[3] = fscc
ref_data[4] = varf
# call user-supplied function to prepare reference image, i.e., center and filter it
vol[iref], cs,fl = ref_ali3d(ref_data)
vol[iref].write_image(os.path.join(outdir, "volf_%s.hdf"%(itout)),-1)
if (apix == 1):
res_msg = "Models filtered at spatial frequency of:\t"
res[iref] = fl
else:
res_msg = "Models filtered at resolution of: \t"
res[iref] = apix / fl
del varf
bcast_EMData_to_all(vol[iref], myid, main_node)
if compare_ref_free != "-1": compare_repro = True
if compare_repro:
outfile_repro = comp_rep(refrings, data, itout, modout, vol[iref], group, nima, nx, myid, main_node, outdir)
mpi_barrier(MPI_COMM_WORLD)
if compare_ref_free != "-1":
ref_free_output = os.path.join(outdir,"ref_free_%s%s"%(itout,modout))
rejects = compare(compare_ref_free, outfile_repro,ref_free_output,yrng[N_step], xrng[N_step], rstep,nx,apix,ref_free_cutoff[N_step], number_of_proc, myid, main_node)
# retrieve alignment params from all processors
par_str = ['xform.projection','ID','group']
if nrefs > 1:
for iref in xrange(nrefs):
par_str.append('eulers_txty.%i'%iref)
if myid == main_node:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else: send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node:
ares = array2string(array(res), precision = 2)
print_msg("%s%s\n\n"%(res_msg,ares))
dummy = EMData()
if full_output:
nimat = EMUtil.get_image_count(stack)
output_file = os.path.join(outdir, "paramout_%s"%itout)
foutput = open(output_file, 'w')
for im in xrange(nimat):
# save the parameters for each of the models
outstring = ""
dummy.read_image(stack,im,True)
param3d = dummy.get_attr('xform.projection')
g = dummy.get_attr("group")
# retrieve alignments in EMAN-format
pE = param3d.get_params('eman')
outstring += "%f\t%f\t%f\t%f\t%f\t%i\n" %(pE["az"], pE["alt"], pE["phi"], pE["tx"], pE["ty"],g)
foutput.write(outstring)
foutput.close()
del dummy
mpi_barrier(MPI_COMM_WORLD)
# mpi_finalize()
if myid == main_node: print_end_msg("ali3d_MPI")
def shiftali_MPI(stack, maskfile=None, maxit=100, CTF=False, snr=1.0, Fourvar=False, search_rng=-1, oneDx=False, search_rng_y=-1):
from applications import MPI_start_end
from utilities import model_circle, model_blank, get_image, peak_search, get_im
from utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type, bcast_number_to_all, bcast_list_to_all
from statistics import varf2d_MPI
from fundamentals import fft, ccf, rot_shift3D, rot_shift2D
from utilities import get_params2D, set_params2D
from utilities import print_msg, print_begin_msg, print_end_msg
import os
import sys
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
from EMAN2 import Processor
from time import time
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
ftp = file_type(stack)
if myid == main_node:
print_begin_msg("shiftali_MPI")
max_iter=int(maxit)
if myid == main_node:
if ftp == "bdb":
from EMAN2db import db_open_dict
dummy = db_open_dict(stack, True)
nima = EMUtil.get_image_count(stack)
else:
nima = 0
nima = bcast_number_to_all(nima, source_node = main_node)
list_of_particles = range(nima)
image_start, image_end = MPI_start_end(nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start: image_end]
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
ima = EMData()
ima.read_image(stack, list_of_particles[0], True)
nx = ima.get_xsize()
ny = ima.get_ysize()
if CTF: ctf_app = ima.get_attr_default('ctf_applied', 2)
del ima
else:
nx = 0
ny = 0
if CTF: ctf_app = 0
nx = bcast_number_to_all(nx, source_node = main_node)
ny = bcast_number_to_all(ny, source_node = main_node)
if CTF:
ctf_app = bcast_number_to_all(ctf_app, source_node = main_node)
if ctf_app > 0: ERROR("data cannot be ctf-applied", "shiftali_MPI", 1, myid)
if maskfile == None:
mrad = min(nx, ny)
mask = model_circle(mrad//2-2, nx, ny)
else:
mask = get_im(maskfile)
if CTF:
from filter import filt_ctf
from morphology import ctf_img
ctf_abs_sum = EMData(nx, ny, 1, False)
ctf_2_sum = EMData(nx, ny, 1, False)
else:
ctf_2_sum = None
from global_def import CACHE_DISABLE
if CACHE_DISABLE:
data = EMData.read_images(stack, list_of_particles)
else:
for i in xrange(number_of_proc):
if myid == i:
data = EMData.read_images(stack, list_of_particles)
if ftp == "bdb": mpi_barrier(MPI_COMM_WORLD)
for im in xrange(len(data)):
data[im].set_attr('ID', list_of_particles[im])
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if CTF:
ctf_params = data[im].get_attr("ctf")
ctfimg = ctf_img(nx, ctf_params, ny=ny)
Util.add_img2(ctf_2_sum, ctfimg)
Util.add_img_abs(ctf_abs_sum, ctfimg)
if CTF:
reduce_EMData_to_root(ctf_2_sum, myid, main_node)
reduce_EMData_to_root(ctf_abs_sum, myid, main_node)
else: ctf_2_sum = None
if CTF:
if myid != main_node:
del ctf_2_sum
del ctf_abs_sum
else:
temp = EMData(nx, ny, 1, False)
for i in xrange(0,nx,2):
for j in xrange(ny):
temp.set_value_at(i,j,snr)
Util.add_img(ctf_2_sum, temp)
del temp
total_iter = 0
# apply initial xform.align2d parameters stored in header
init_params = []
for im in xrange(len(data)):
t = data[im].get_attr('xform.align2d')
init_params.append(t)
p = t.get_params("2d")
data[im] = rot_shift2D(data[im], p['alpha'], sx=p['tx'], sy=p['ty'], mirror=p['mirror'], scale=p['scale'])
# fourier transform all images, and apply ctf if CTF
for im in xrange(len(data)):
if CTF:
ctf_params = data[im].get_attr("ctf")
data[im] = filt_ctf(fft(data[im]), ctf_params)
else:
data[im] = fft(data[im])
sx_sum=0
sy_sum=0
sx_sum_total=0
sy_sum_total=0
shift_x = [0.0]*len(data)
shift_y = [0.0]*len(data)
ishift_x = [0.0]*len(data)
ishift_y = [0.0]*len(data)
for Iter in xrange(max_iter):
if myid == main_node:
start_time = time()
print_msg("Iteration #%4d\n"%(total_iter))
total_iter += 1
avg = EMData(nx, ny, 1, False)
for im in data: Util.add_img(avg, im)
reduce_EMData_to_root(avg, myid, main_node)
if myid == main_node:
if CTF:
tavg = Util.divn_filter(avg, ctf_2_sum)
else: tavg = Util.mult_scalar(avg, 1.0/float(nima))
else:
tavg = EMData(nx, ny, 1, False)
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
# normalize and mask tavg in real space
tavg = fft(tavg)
stat = Util.infomask( tavg, mask, False )
tavg -= stat[0]
Util.mul_img(tavg, mask)
# For testing purposes: shift tavg to some random place and see if the centering is still correct
#tavg = rot_shift3D(tavg,sx=3,sy=-4)
tavg = fft(tavg)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
sx_sum=0
sy_sum=0
if search_rng > 0: nwx = 2*search_rng+1
else: nwx = nx
if search_rng_y > 0: nwy = 2*search_rng_y+1
else: nwy = ny
not_zero = 0
for im in xrange(len(data)):
if oneDx:
ctx = Util.window(ccf(data[im],tavg),nwx,1)
p1 = peak_search(ctx)
p1_x = -int(p1[0][3])
ishift_x[im] = p1_x
sx_sum += p1_x
else:
p1 = peak_search(Util.window(ccf(data[im],tavg), nwx,nwy))
p1_x = -int(p1[0][4])
p1_y = -int(p1[0][5])
ishift_x[im] = p1_x
ishift_y[im] = p1_y
sx_sum += p1_x
sy_sum += p1_y
if not_zero == 0:
if (not(ishift_x[im] == 0.0)) or (not(ishift_y[im] == 0.0)):
not_zero = 1
sx_sum = mpi_reduce(sx_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if not oneDx:
sy_sum = mpi_reduce(sy_sum, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
sx_sum_total = int(sx_sum[0])
if not oneDx:
sy_sum_total = int(sy_sum[0])
else:
sx_sum_total = 0
sy_sum_total = 0
sx_sum_total = bcast_number_to_all(sx_sum_total, source_node = main_node)
if not oneDx:
sy_sum_total = bcast_number_to_all(sy_sum_total, source_node = main_node)
sx_ave = round(float(sx_sum_total)/nima)
sy_ave = round(float(sy_sum_total)/nima)
for im in xrange(len(data)):
p1_x = ishift_x[im] - sx_ave
p1_y = ishift_y[im] - sy_ave
params2 = {"filter_type" : Processor.fourier_filter_types.SHIFT, "x_shift" : p1_x, "y_shift" : p1_y, "z_shift" : 0.0}
data[im] = Processor.EMFourierFilter(data[im], params2)
shift_x[im] += p1_x
shift_y[im] += p1_y
# stop if all shifts are zero
not_zero = mpi_reduce(not_zero, 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
not_zero_all = int(not_zero[0])
else:
not_zero_all = 0
not_zero_all = bcast_number_to_all(not_zero_all, source_node = main_node)
if myid == main_node:
print_msg("Time of iteration = %12.2f\n"%(time()-start_time))
start_time = time()
if not_zero_all == 0: break
#for im in xrange(len(data)): data[im] = fft(data[im]) This should not be required as only header information is used
# combine shifts found with the original parameters
for im in xrange(len(data)):
t0 = init_params[im]
t1 = Transform()
t1.set_params({"type":"2D","alpha":0,"scale":t0.get_scale(),"mirror":0,"tx":shift_x[im],"ty":shift_y[im]})
# combine t0 and t1
tt = t1*t0
data[im].set_attr("xform.align2d", tt)
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(MPI_COMM_WORLD)
par_str = ["xform.align2d", "ID"]
if myid == main_node:
from utilities import file_type
if(file_type(stack) == "bdb"):
from utilities import recv_attr_dict_bdb
recv_attr_dict_bdb(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else:
from utilities import recv_attr_dict
recv_attr_dict(main_node, stack, data, par_str, image_start, image_end, number_of_proc)
else: send_attr_dict(main_node, data, par_str, image_start, image_end)
if myid == main_node: print_end_msg("shiftali_MPI")
def compare(compare_ref_free, outfile_repro,ref_free_output,yrng, xrng, rstep,nx,apix,ref_free_cutoff, nproc, myid, main_node):
from alignment import Numrinit, ringwe, Applyws
from random import seed, randint
from utilities import get_params2D, set_params2D, model_circle, inverse_transform2, combine_params2
from fundamentals import rot_shift2D
from mpi import MPI_COMM_WORLD, mpi_barrier, mpi_bcast, MPI_INT
from statistics import fsc_mask
from filter import fit_tanh
from numpy import array
fout = "%s.hdf" % ref_free_output
frc_out = "%s_frc" % ref_free_output
res_out = "%s_res" % ref_free_output
nima = EMUtil.get_image_count(compare_ref_free)
image_start, image_end = MPI_start_end(nima, nproc, myid)
ima = EMData()
ima.read_image(compare_ref_free, image_start)
last_ring = nx/2-2
first_ring = 1
mask = model_circle(last_ring, nx, nx)
refi = []
numref = EMUtil.get_image_count(outfile_repro)
cnx = nx/2 +1
cny = cnx
mode = "F"
numr = Numrinit(first_ring, last_ring, rstep, mode)
wr = ringwe(numr, mode)
ima.to_zero()
for j in xrange(numref):
temp = EMData()
temp.read_image(outfile_repro, j)
# even, odd, numer of even, number of images. After frc, totav
refi.append(temp)
# for each node read its share of data
data = EMData.read_images(compare_ref_free, range(image_start, image_end))
for im in xrange(image_start, image_end):
data[im-image_start].set_attr('ID', im)
set_params2D(data[im-image_start],[0,0,0,0,1])
ringref = []
for j in xrange(numref):
refi[j].process_inplace("normalize.mask", {"mask":mask, "no_sigma":1}) # normalize reference images to N(0,1)
cimage = Util.Polar2Dm(refi[j], cnx, cny, numr, mode)
Util.Frngs(cimage, numr)
Applyws(cimage, numr, wr)
ringref.append(cimage)
if myid == main_node: seed(1000)
data_shift = []
frc = []
res = []
for im in xrange(image_start, image_end):
alpha, sx, sy, mirror, scale = get_params2D(data[im-image_start])
alphai, sxi, syi, scalei = inverse_transform2(alpha, sx, sy, 1.0)
# normalize
data[im-image_start].process_inplace("normalize.mask", {"mask":mask, "no_sigma":1}) # subtract average under the mask
# align current image to the reference
[angt, sxst, syst, mirrort, xiref, peakt] = Util.multiref_polar_ali_2d(data[im-image_start], ringref, xrng, yrng, 1, mode, numr, cnx+sxi, cny+syi)
iref = int(xiref)
[alphan, sxn, syn, mn] = combine_params2(0.0, -sxi, -syi, 0, angt, sxst, syst, (int)(mirrort))
set_params2D(data[im-image_start], [alphan, sxn, syn, int(mn), scale])
temp = rot_shift2D(data[im-image_start], alphan, sxn, syn, mn)
temp.set_attr('assign',iref)
tfrc = fsc_mask(temp,refi[iref],mask = mask)
temp.set_attr('frc',tfrc[1])
res = fit_tanh(tfrc)
temp.set_attr('res',res)
data_shift.append(temp)
for node in xrange(nproc):
if myid == node:
for image in data_shift:
image.write_image(fout,-1)
refindex = image.get_attr('assign')
refi[refindex].write_image(fout,-1)
mpi_barrier(MPI_COMM_WORLD)
rejects = []
if myid == main_node:
a = EMData()
index = 0
frc = []
res = []
temp = []
classes = []
for im in xrange(nima):
a.read_image(fout, index)
frc.append(a.get_attr("frc"))
if ref_free_cutoff != -1: classes.append(a.get_attr("class_ptcl_idxs"))
tmp = a.get_attr("res")
temp.append(tmp[0])
res.append("%12f" %(apix/tmp[0]))
res.append("\n")
index = index + 2
res_num = array(temp)
mean_score = res_num.mean(axis=0)
std_score = res_num.std(axis=0)
std = std_score / 2
if ref_free_cutoff !=-1:
cutoff = mean_score - std * ref_free_cutoff
reject = res_num < cutoff
index = 0
for i in reject:
if i: rejects.extend(classes[index])
index = index + 1
rejects.sort()
length = mpi_bcast(len(rejects),1,MPI_INT,main_node, MPI_COMM_WORLD)
rejects = mpi_bcast(rejects,length , MPI_INT, main_node, MPI_COMM_WORLD)
del a
fout_frc = open(frc_out,'w')
fout_res = open(res_out,'w')
fout_res.write("".join(res))
temp = zip(*frc)
datstrings = []
for i in temp:
for j in i:
datstrings.append(" %12f" % (j))
datstrings.append("\n")
fout_frc.write("".join(datstrings))
fout_frc.close()
del refi
del ringref
return rejects
def cml_open_proj(stack, ir, ou, lf, hf, dpsi = 1):
from projection import cml_sinogram
from utilities import model_circle, get_params_proj, model_blank, get_im
from fundamentals import fftip
from filter import filt_tanh
# number of projections
if type(stack) == type(""): nprj = EMUtil.get_image_count(stack)
else: nprj = len(stack)
Prj = [] # list of projections
Ori = [-1] * 4 * nprj # orientation intial (phi, theta, psi, index) for each projection
for i in xrange(nprj):
image = get_im(stack, i)
# read initial angles if given
try: Ori[4*i], Ori[4*i+1], Ori[4*i+2], s2x, s2y = get_params_proj(image)
except: pass
if(i == 0):
nx = image.get_xsize()
if(ou < 1): ou = nx // 2 - 1
diameter = int(2 * ou)
mask2D = model_circle(ou, nx, nx)
if ir > 0: mask2D -= model_circle(ir, nx, nx)
# normalize under the mask
[mean_a, sigma, imin, imax] = Util.infomask(image, mask2D, True)
image -= mean_a
Util.mul_scalar(image, 1.0/sigma)
Util.mul_img(image, mask2D)
# sinogram
sino = cml_sinogram(image, diameter, dpsi)
# prepare the cut positions in order to filter (lf: low freq; hf: high freq)
ihf = min(int(2 * hf * diameter), diameter + (diameter + 1) % 2)
ihf = ihf + (ihf + 1) % 2 # index ihf must be odd to take the img part
ilf = max(int(2 * lf * diameter), 0)
ilf = ilf + ilf % 2 # index ilf must be even to fall in the real part
bdf = ihf - ilf + 1
# process lines
nxe = sino.get_xsize()
nye = sino.get_ysize()
prj = model_blank(bdf, 2*nye)
pp = model_blank(nxe, 2*nye)
for li in xrange(nye):
# get the line li
line = Util.window(sino, nxe, 1, 1, 0, li-nye//2, 0)
# u2 (not improve the results)
#line = filt_tanh(line, ou / float(nx), ou / float(nx))
# normalize this line
[mean_l, sigma_l, imin, imax] = Util.infomask(line, None, True)
line = (line - mean_l) / sigma_l
# fft
fftip(line)
# filter (cut part of coef) and create mirror line
Util.cml_prepare_line(prj, line, ilf, ihf, li, nye)
# store the projection
Prj.append(prj)
return Prj, Ori
def main():
import global_def
from optparse import OptionParser
from EMAN2 import EMUtil
import os
import sys
from time import time
progname = os.path.basename(sys.argv[0])
usage = progname + " proj_stack output_averages --MPI"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--img_per_group",
type="int",
default=100,
help="number of images per group")
parser.add_option("--radius",
type="int",
default=-1,
help="radius for alignment")
parser.add_option(
"--xr",
type="string",
default="2 1",
help="range for translation search in x direction, search is +/xr")
parser.add_option(
"--yr",
type="string",
default="-1",
help=
"range for translation search in y direction, search is +/yr (default = same as xr)"
)
parser.add_option(
"--ts",
type="string",
default="1 0.5",
help=
"step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional"
)
parser.add_option(
"--iter",
type="int",
default=30,
help="number of iterations within alignment (default = 30)")
parser.add_option(
"--num_ali",
type="int",
default=5,
help="number of alignments performed for stability (default = 5)")
parser.add_option("--thld_err",
type="float",
default=1.0,
help="threshold of pixel error (default = 1.732)")
parser.add_option(
"--grouping",
type="string",
default="GRP",
help=
"do grouping of projections: PPR - per projection, GRP - different size groups, exclusive (default), GEV - grouping equal size"
)
parser.add_option(
"--delta",
type="float",
default=-1.0,
help="angular step for reference projections (required for GEV method)"
)
parser.add_option(
"--fl",
type="float",
default=0.3,
help="cut-off frequency of hyperbolic tangent low-pass Fourier filter")
parser.add_option(
"--aa",
type="float",
default=0.2,
help="fall-off of hyperbolic tangent low-pass Fourier filter")
parser.add_option("--CTF",
action="store_true",
default=False,
help="Consider CTF correction during the alignment ")
parser.add_option("--MPI",
action="store_true",
default=False,
help="use MPI version")
(options, args) = parser.parse_args()
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, MPI_COMM_WORLD
from mpi import mpi_barrier, mpi_send, mpi_recv, mpi_bcast, MPI_INT, mpi_finalize, MPI_FLOAT
from applications import MPI_start_end, within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import send_EMData, recv_EMData
from utilities import get_image, bcast_number_to_all, set_params2D, get_params2D
from utilities import group_proj_by_phitheta, model_circle, get_input_from_string
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
if len(args) == 2:
stack = args[0]
outdir = args[1]
else:
ERROR("incomplete list of arguments", "sxproj_stability", 1, myid=myid)
exit()
if not options.MPI:
ERROR("Non-MPI not supported!", "sxproj_stability", myid=myid)
exit()
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
#if os.path.exists(outdir): ERROR('Output directory exists, please change the name and restart the program', "sxproj_stability", 1, myid)
#mpi_barrier(MPI_COMM_WORLD)
img_per_grp = options.img_per_group
radius = options.radius
ite = options.iter
num_ali = options.num_ali
thld_err = options.thld_err
xrng = get_input_from_string(options.xr)
if options.yr == "-1": yrng = xrng
else: yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
else:
nima = 0
nx = 0
ny = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
if radius == -1: radius = nx / 2 - 2
mask = model_circle(radius, nx, nx)
st = time()
if options.grouping == "GRP":
if myid == main_node:
print " A ", myid, " ", time() - st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp[
"psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
# Here is where the grouping is done, I didn't put enough annotation in the group_proj_by_phitheta,
# So I will briefly explain it here
# proj_list : Returns a list of list of particle numbers, each list contains img_per_grp particle numbers
# except for the last one. Depending on the number of particles left, they will either form a
# group or append themselves to the last group
# angle_list : Also returns a list of list, each list contains three numbers (phi, theta, delta), (phi,
# theta) is the projection angle of the center of the group, delta is the range of this group
# mirror_list: Also returns a list of list, each list contains img_per_grp True or False, which indicates
# whether it should take mirror position.
# In this program angle_list and mirror list are not of interest.
proj_list_all, angle_list, mirror_list = group_proj_by_phitheta(
proj_params, img_per_grp=img_per_grp)
del proj_params
print " B number of groups ", myid, " ", len(
proj_list_all), time() - st
mpi_barrier(MPI_COMM_WORLD)
# Number of groups, actually there could be one or two more groups, since the size of the remaining group varies
# we will simply assign them to main node.
n_grp = nima / img_per_grp - 1
# Divide proj_list_all equally to all nodes, and becomes proj_list
proj_list = []
for i in xrange(n_grp):
proc_to_stay = i % number_of_proc
if proc_to_stay == main_node:
if myid == main_node: proj_list.append(proj_list_all[i])
elif myid == main_node:
mpi_send(len(proj_list_all[i]), 1, MPI_INT, proc_to_stay,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(proj_list_all[i], len(proj_list_all[i]), MPI_INT,
proc_to_stay, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
elif myid == proc_to_stay:
img_per_grp = mpi_recv(1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
img_per_grp = int(img_per_grp[0])
temp = mpi_recv(img_per_grp, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
proj_list.append(map(int, temp))
del temp
mpi_barrier(MPI_COMM_WORLD)
print " C ", myid, " ", time() - st
if myid == main_node:
# Assign the remaining groups to main_node
for i in xrange(n_grp, len(proj_list_all)):
proj_list.append(proj_list_all[i])
del proj_list_all, angle_list, mirror_list
# Compute stability per projection projection direction, equal number assigned, thus overlaps
elif options.grouping == "GEV":
if options.delta == -1.0:
ERROR(
"Angular step for reference projections is required for GEV method",
"sxproj_stability", 1)
from utilities import even_angles, nearestk_to_refdir, getvec
refproj = even_angles(options.delta)
img_begin, img_end = MPI_start_end(len(refproj), number_of_proc, myid)
# Now each processor keeps its own share of reference projections
refprojdir = refproj[img_begin:img_end]
del refproj
ref_ang = [0.0] * (len(refprojdir) * 2)
for i in xrange(len(refprojdir)):
ref_ang[i * 2] = refprojdir[0][0]
ref_ang[i * 2 + 1] = refprojdir[0][1] + i * 0.1
print " A ", myid, " ", time() - st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
# the solution below is very slow, do not use it unless there is a problem with the i/O
"""
for i in xrange(number_of_proc):
if myid == i:
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
mpi_barrier(MPI_COMM_WORLD)
"""
print " B ", myid, " ", time() - st
proj_ang = [0.0] * (nima * 2)
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
proj_ang[i * 2] = dp["phi"]
proj_ang[i * 2 + 1] = dp["theta"]
print " C ", myid, " ", time() - st
asi = Util.nearestk_to_refdir(proj_ang, ref_ang, img_per_grp)
del proj_ang, ref_ang
proj_list = []
for i in xrange(len(refprojdir)):
proj_list.append(asi[i * img_per_grp:(i + 1) * img_per_grp])
del asi
print " D ", myid, " ", time() - st
#from sys import exit
#exit()
# Compute stability per projection
elif options.grouping == "PPR":
print " A ", myid, " ", time() - st
proj_attr = EMUtil.get_all_attributes(stack, "xform.projection")
print " B ", myid, " ", time() - st
proj_params = []
for i in xrange(nima):
dp = proj_attr[i].get_params("spider")
phi, theta, psi, s2x, s2y = dp["phi"], dp["theta"], dp[
"psi"], -dp["tx"], -dp["ty"]
proj_params.append([phi, theta, psi, s2x, s2y])
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
print " C ", myid, " ", time() - st
from utilities import nearest_proj
proj_list, mirror_list = nearest_proj(
proj_params, img_per_grp,
range(img_begin, img_begin + 1)) #range(img_begin, img_end))
refprojdir = proj_params[img_begin:img_end]
del proj_params, mirror_list
print " D ", myid, " ", time() - st
else:
ERROR("Incorrect projection grouping option", "sxproj_stability", 1)
"""
from utilities import write_text_file
for i in xrange(len(proj_list)):
write_text_file(proj_list[i],"projlist%06d_%04d"%(i,myid))
"""
###########################################################################################################
# Begin stability test
from utilities import get_params_proj, read_text_file
#if myid == 0:
# from utilities import read_text_file
# proj_list[0] = map(int, read_text_file("lggrpp0.txt"))
from utilities import model_blank
aveList = [model_blank(nx, ny)] * len(proj_list)
if options.grouping == "GRP":
refprojdir = [[0.0, 0.0, -1.0]] * len(proj_list)
for i in xrange(len(proj_list)):
print " E ", myid, " ", time() - st
class_data = EMData.read_images(stack, proj_list[i])
#print " R ",myid," ",time()-st
if options.CTF:
from filter import filt_ctf
for im in xrange(len(class_data)): # MEM LEAK!!
atemp = class_data[im].copy()
btemp = filt_ctf(atemp, atemp.get_attr("ctf"), binary=1)
class_data[im] = btemp
#class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
try:
t = im.get_attr(
"xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
#print " F ",myid," ",time()-st
# Here, we perform realignment num_ali times
all_ali_params = []
for j in xrange(num_ali):
if (xrng[0] == 0.0 and yrng[0] == 0.0):
avet = ali2d_ras(class_data,
randomize=True,
ir=1,
ou=radius,
rs=1,
step=1.0,
dst=90.0,
maxit=ite,
check_mirror=True,
FH=options.fl,
FF=options.aa)
else:
avet = within_group_refinement(class_data, mask, True, 1,
radius, 1, xrng, yrng, step,
90.0, ite, options.fl,
options.aa)
ali_params = []
for im in xrange(len(class_data)):
alpha, sx, sy, mirror, scale = get_params2D(class_data[im])
ali_params.extend([alpha, sx, sy, mirror])
all_ali_params.append(ali_params)
#aveList[i] = avet
#print " G ",myid," ",time()-st
del ali_params
# We determine the stability of this group here.
# stable_set contains all particles deemed stable, it is a list of list
# each list has two elements, the first is the pixel error, the second is the image number
# stable_set is sorted based on pixel error
#from utilities import write_text_file
#write_text_file(all_ali_params, "all_ali_params%03d.txt"%myid)
stable_set, mir_stab_rate, average_pix_err = multi_align_stability(
all_ali_params, 0.0, 10000.0, thld_err, False, 2 * radius + 1)
#print " H ",myid," ",time()-st
if (len(stable_set) > 5):
stable_set_id = []
members = []
pix_err = []
# First put the stable members into attr 'members' and 'pix_err'
for s in stable_set:
# s[1] - number in this subset
stable_set_id.append(s[1])
# the original image number
members.append(proj_list[i][s[1]])
pix_err.append(s[0])
# Then put the unstable members into attr 'members' and 'pix_err'
from fundamentals import rot_shift2D
avet.to_zero()
if options.grouping == "GRP":
aphi = 0.0
atht = 0.0
vphi = 0.0
vtht = 0.0
l = -1
for j in xrange(len(proj_list[i])):
# Here it will only work if stable_set_id is sorted in the increasing number, see how l progresses
if j in stable_set_id:
l += 1
avet += rot_shift2D(class_data[j], stable_set[l][2][0],
stable_set[l][2][1],
stable_set[l][2][2],
stable_set[l][2][3])
if options.grouping == "GRP":
phi, theta, psi, sxs, sys = get_params_proj(
class_data[j])
if (theta > 90.0):
phi = (phi + 540.0) % 360.0
theta = 180.0 - theta
aphi += phi
atht += theta
vphi += phi * phi
vtht += theta * theta
else:
members.append(proj_list[i][j])
pix_err.append(99999.99)
aveList[i] = avet.copy()
if l > 1:
l += 1
aveList[i] /= l
if options.grouping == "GRP":
aphi /= l
atht /= l
vphi = (vphi - l * aphi * aphi) / l
vtht = (vtht - l * atht * atht) / l
from math import sqrt
refprojdir[i] = [
aphi, atht,
(sqrt(max(vphi, 0.0)) + sqrt(max(vtht, 0.0))) / 2.0
]
# Here more information has to be stored, PARTICULARLY WHAT IS THE REFERENCE DIRECTION
aveList[i].set_attr('members', members)
aveList[i].set_attr('refprojdir', refprojdir[i])
aveList[i].set_attr('pixerr', pix_err)
else:
print " empty group ", i, refprojdir[i]
aveList[i].set_attr('members', [-1])
aveList[i].set_attr('refprojdir', refprojdir[i])
aveList[i].set_attr('pixerr', [99999.])
del class_data
if myid == main_node:
km = 0
for i in xrange(number_of_proc):
if i == main_node:
for im in xrange(len(aveList)):
aveList[im].write_image(args[1], km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nl = int(nl[0])
for im in xrange(nl):
ave = recv_EMData(i, im + i + 70000)
nm = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('pixerr', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
ave.write_image(args[1], km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL,
MPI_COMM_WORLD)
for im in xrange(len(aveList)):
send_EMData(aveList[im], main_node, im + myid + 70000)
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
members = aveList[im].get_attr('pixerr')
mpi_send(members, len(members), MPI_FLOAT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
except:
mpi_send([-999.0, -999.0, -999.0], 3, MPI_FLOAT, main_node,
SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
global_def.BATCH = False
mpi_barrier(MPI_COMM_WORLD)
from mpi import mpi_finalize
mpi_finalize()
def main():
def params_3D_2D_NEW(phi, theta, psi, s2x, s2y, mirror):
# the final ali2d parameters already combine shifts operation first and rotation operation second for parameters converted from 3D
if mirror:
m = 1
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 540.0-psi, 0, 0, 1.0)
else:
m = 0
alpha, sx, sy, scalen = compose_transform2(0, s2x, s2y, 1.0, 360.0-psi, 0, 0, 1.0)
return alpha, sx, sy, m
progname = os.path.basename(sys.argv[0])
usage = progname + " prj_stack --ave2D= --var2D= --ave3D= --var3D= --img_per_grp= --fl= --aa= --sym=symmetry --CTF"
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--output_dir", type="string" , default="./", help="Output directory")
parser.add_option("--ave2D", type="string" , default=False, help="Write to the disk a stack of 2D averages")
parser.add_option("--var2D", type="string" , default=False, help="Write to the disk a stack of 2D variances")
parser.add_option("--ave3D", type="string" , default=False, help="Write to the disk reconstructed 3D average")
parser.add_option("--var3D", type="string" , default=False, help="Compute 3D variability (time consuming!)")
parser.add_option("--img_per_grp", type="int" , default=100, help="Number of neighbouring projections.(Default is 100)")
parser.add_option("--no_norm", action="store_true", default=False, help="Do not use normalization.(Default is to apply normalization)")
#parser.add_option("--radius", type="int" , default=-1 , help="radius for 3D variability" )
parser.add_option("--npad", type="int" , default=2 , help="Number of time to pad the original images.(Default is 2 times padding)")
parser.add_option("--sym" , type="string" , default="c1", help="Symmetry. (Default is no symmetry)")
parser.add_option("--fl", type="float" , default=0.0, help="Low pass filter cutoff in absolute frequency (0.0 - 0.5) and is applied to decimated images. (Default - no filtration)")
parser.add_option("--aa", type="float" , default=0.02 , help="Fall off of the filter. Use default value if user has no clue about falloff (Default value is 0.02)")
parser.add_option("--CTF", action="store_true", default=False, help="Use CFT correction.(Default is no CTF correction)")
#parser.add_option("--MPI" , action="store_true", default=False, help="use MPI version")
#parser.add_option("--radiuspca", type="int" , default=-1 , help="radius for PCA" )
#parser.add_option("--iter", type="int" , default=40 , help="maximum number of iterations (stop criterion of reconstruction process)" )
#parser.add_option("--abs", type="float" , default=0.0 , help="minimum average absolute change of voxels' values (stop criterion of reconstruction process)" )
#parser.add_option("--squ", type="float" , default=0.0 , help="minimum average squared change of voxels' values (stop criterion of reconstruction process)" )
parser.add_option("--VAR" , action="store_true", default=False, help="Stack of input consists of 2D variances (Default False)")
parser.add_option("--decimate", type ="float", default=0.25, help="Image decimate rate, a number less than 1. (Default is 0.25)")
parser.add_option("--window", type ="int", default=0, help="Target image size relative to original image size. (Default value is zero.)")
#parser.add_option("--SND", action="store_true", default=False, help="compute squared normalized differences (Default False)")
#parser.add_option("--nvec", type="int" , default=0 , help="Number of eigenvectors, (Default = 0 meaning no PCA calculated)")
parser.add_option("--symmetrize", action="store_true", default=False, help="Prepare input stack for handling symmetry (Default False)")
parser.add_option("--overhead", type ="float", default=0.5, help="python overhead per CPU.")
(options,args) = parser.parse_args()
#####
from mpi import mpi_init, mpi_comm_rank, mpi_comm_size, mpi_recv, MPI_COMM_WORLD
from mpi import mpi_barrier, mpi_reduce, mpi_bcast, mpi_send, MPI_FLOAT, MPI_SUM, MPI_INT, MPI_MAX
#from mpi import *
from applications import MPI_start_end
from reconstruction import recons3d_em, recons3d_em_MPI
from reconstruction import recons3d_4nn_MPI, recons3d_4nn_ctf_MPI
from utilities import print_begin_msg, print_end_msg, print_msg
from utilities import read_text_row, get_image, get_im, wrap_mpi_send, wrap_mpi_recv
from utilities import bcast_EMData_to_all, bcast_number_to_all
from utilities import get_symt
# This is code for handling symmetries by the above program. To be incorporated. PAP 01/27/2015
from EMAN2db import db_open_dict
# Set up global variables related to bdb cache
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
# Set up global variables related to ERROR function
global_def.BATCH = True
# detect if program is running under MPI
RUNNING_UNDER_MPI = "OMPI_COMM_WORLD_SIZE" in os.environ
if RUNNING_UNDER_MPI: global_def.MPI = True
if options.output_dir =="./": current_output_dir = os.path.abspath(options.output_dir)
else: current_output_dir = options.output_dir
if options.symmetrize :
if RUNNING_UNDER_MPI:
try:
sys.argv = mpi_init(len(sys.argv), sys.argv)
try:
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
if( number_of_proc > 1 ):
ERROR("Cannot use more than one CPU for symmetry preparation","sx3dvariability",1)
except:
pass
except:
pass
if not os.path.exists(current_output_dir): os.mkdir(current_output_dir)
# Input
#instack = "Clean_NORM_CTF_start_wparams.hdf"
#instack = "bdb:data"
from logger import Logger,BaseLogger_Files
if os.path.exists(os.path.join(current_output_dir, "log.txt")): os.remove(os.path.join(current_output_dir, "log.txt"))
log_main=Logger(BaseLogger_Files())
log_main.prefix = os.path.join(current_output_dir, "./")
instack = args[0]
sym = options.sym.lower()
if( sym == "c1" ):
ERROR("There is no need to symmetrize stack for C1 symmetry","sx3dvariability",1)
line =""
for a in sys.argv:
line +=" "+a
log_main.add(line)
if(instack[:4] !="bdb:"):
#if output_dir =="./": stack = "bdb:data"
stack = "bdb:"+current_output_dir+"/data"
delete_bdb(stack)
junk = cmdexecute("sxcpy.py "+instack+" "+stack)
else: stack = instack
qt = EMUtil.get_all_attributes(stack,'xform.projection')
na = len(qt)
ts = get_symt(sym)
ks = len(ts)
angsa = [None]*na
for k in range(ks):
#Qfile = "Q%1d"%k
#if options.output_dir!="./": Qfile = os.path.join(options.output_dir,"Q%1d"%k)
Qfile = os.path.join(current_output_dir, "Q%1d"%k)
#delete_bdb("bdb:Q%1d"%k)
delete_bdb("bdb:"+Qfile)
#junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:"+Qfile)
#DB = db_open_dict("bdb:Q%1d"%k)
DB = db_open_dict("bdb:"+Qfile)
for i in range(na):
ut = qt[i]*ts[k]
DB.set_attr(i, "xform.projection", ut)
#bt = ut.get_params("spider")
#angsa[i] = [round(bt["phi"],3)%360.0, round(bt["theta"],3)%360.0, bt["psi"], -bt["tx"], -bt["ty"]]
#write_text_row(angsa, 'ptsma%1d.txt'%k)
#junk = cmdexecute("e2bdb.py "+stack+" --makevstack=bdb:Q%1d"%k)
#junk = cmdexecute("sxheader.py bdb:Q%1d --params=xform.projection --import=ptsma%1d.txt"%(k,k))
DB.close()
#if options.output_dir =="./": delete_bdb("bdb:sdata")
delete_bdb("bdb:" + current_output_dir + "/"+"sdata")
#junk = cmdexecute("e2bdb.py . --makevstack=bdb:sdata --filt=Q")
sdata = "bdb:"+current_output_dir+"/"+"sdata"
print(sdata)
junk = cmdexecute("e2bdb.py " + current_output_dir +" --makevstack="+sdata +" --filt=Q")
#junk = cmdexecute("ls EMAN2DB/sdata*")
#a = get_im("bdb:sdata")
a = get_im(sdata)
a.set_attr("variabilitysymmetry",sym)
#a.write_image("bdb:sdata")
a.write_image(sdata)
else:
from fundamentals import window2d
sys.argv = mpi_init(len(sys.argv), sys.argv)
myid = mpi_comm_rank(MPI_COMM_WORLD)
number_of_proc = mpi_comm_size(MPI_COMM_WORLD)
main_node = 0
shared_comm = mpi_comm_split_type(MPI_COMM_WORLD, MPI_COMM_TYPE_SHARED, 0, MPI_INFO_NULL)
myid_on_node = mpi_comm_rank(shared_comm)
no_of_processes_per_group = mpi_comm_size(shared_comm)
masters_from_groups_vs_everything_else_comm = mpi_comm_split(MPI_COMM_WORLD, main_node == myid_on_node, myid_on_node)
color, no_of_groups, balanced_processor_load_on_nodes = get_colors_and_subsets(main_node, MPI_COMM_WORLD, myid, \
shared_comm, myid_on_node, masters_from_groups_vs_everything_else_comm)
overhead_loading = options.overhead*number_of_proc
#memory_per_node = options.memory_per_node
#if memory_per_node == -1.: memory_per_node = 2.*no_of_processes_per_group
keepgoing = 1
current_window = options.window
current_decimate = options.decimate
if len(args) == 1: stack = args[0]
else:
print(( "usage: " + usage))
print(( "Please run '" + progname + " -h' for detailed options"))
return 1
t0 = time()
# obsolete flags
options.MPI = True
#options.nvec = 0
options.radiuspca = -1
options.iter = 40
options.abs = 0.0
options.squ = 0.0
if options.fl > 0.0 and options.aa == 0.0:
ERROR("Fall off has to be given for the low-pass filter", "sx3dvariability", 1, myid)
#if options.VAR and options.SND:
# ERROR("Only one of var and SND can be set!", "sx3dvariability", myid)
if options.VAR and (options.ave2D or options.ave3D or options.var2D):
ERROR("When VAR is set, the program cannot output ave2D, ave3D or var2D", "sx3dvariability", 1, myid)
#if options.SND and (options.ave2D or options.ave3D):
# ERROR("When SND is set, the program cannot output ave2D or ave3D", "sx3dvariability", 1, myid)
#if options.nvec > 0 :
# ERROR("PCA option not implemented", "sx3dvariability", 1, myid)
#if options.nvec > 0 and options.ave3D == None:
# ERROR("When doing PCA analysis, one must set ave3D", "sx3dvariability", 1, myid)
if current_decimate>1.0 or current_decimate<0.0:
ERROR("Decimate rate should be a value between 0.0 and 1.0", "sx3dvariability", 1, myid)
if current_window < 0.0:
ERROR("Target window size should be always larger than zero", "sx3dvariability", 1, myid)
if myid == main_node:
img = get_image(stack, 0)
nx = img.get_xsize()
ny = img.get_ysize()
if(min(nx, ny) < current_window): keepgoing = 0
keepgoing = bcast_number_to_all(keepgoing, main_node, MPI_COMM_WORLD)
if keepgoing == 0: ERROR("The target window size cannot be larger than the size of decimated image", "sx3dvariability", 1, myid)
import string
options.sym = options.sym.lower()
# if global_def.CACHE_DISABLE:
# from utilities import disable_bdb_cache
# disable_bdb_cache()
# global_def.BATCH = True
if myid == main_node:
if not os.path.exists(current_output_dir): os.mkdir(current_output_dir)# Never delete output_dir in the program!
img_per_grp = options.img_per_grp
#nvec = options.nvec
radiuspca = options.radiuspca
from logger import Logger,BaseLogger_Files
#if os.path.exists(os.path.join(options.output_dir, "log.txt")): os.remove(os.path.join(options.output_dir, "log.txt"))
log_main=Logger(BaseLogger_Files())
log_main.prefix = os.path.join(current_output_dir, "./")
if myid == main_node:
line = ""
for a in sys.argv: line +=" "+a
log_main.add(line)
log_main.add("-------->>>Settings given by all options<<<-------")
log_main.add("Symmetry : %s"%options.sym)
log_main.add("Input stack : %s"%stack)
log_main.add("Output_dir : %s"%current_output_dir)
if options.ave3D: log_main.add("Ave3d : %s"%options.ave3D)
if options.var3D: log_main.add("Var3d : %s"%options.var3D)
if options.ave2D: log_main.add("Ave2D : %s"%options.ave2D)
if options.var2D: log_main.add("Var2D : %s"%options.var2D)
if options.VAR: log_main.add("VAR : True")
else: log_main.add("VAR : False")
if options.CTF: log_main.add("CTF correction : True ")
else: log_main.add("CTF correction : False ")
log_main.add("Image per group : %5d"%options.img_per_grp)
log_main.add("Image decimate rate : %4.3f"%current_decimate)
log_main.add("Low pass filter : %4.3f"%options.fl)
current_fl = options.fl
if current_fl == 0.0: current_fl = 0.5
log_main.add("Current low pass filter is equivalent to cutoff frequency %4.3f for original image size"%round((current_fl*current_decimate),3))
log_main.add("Window size : %5d "%current_window)
log_main.add("sx3dvariability begins")
symbaselen = 0
if myid == main_node:
nima = EMUtil.get_image_count(stack)
img = get_image(stack)
nx = img.get_xsize()
ny = img.get_ysize()
nnxo = nx
nnyo = ny
if options.sym != "c1" :
imgdata = get_im(stack)
try:
i = imgdata.get_attr("variabilitysymmetry").lower()
if(i != options.sym):
ERROR("The symmetry provided does not agree with the symmetry of the input stack", "sx3dvariability", 1, myid)
except:
ERROR("Input stack is not prepared for symmetry, please follow instructions", "sx3dvariability", 1, myid)
from utilities import get_symt
i = len(get_symt(options.sym))
if((nima/i)*i != nima):
ERROR("The length of the input stack is incorrect for symmetry processing", "sx3dvariability", 1, myid)
symbaselen = nima/i
else: symbaselen = nima
else:
nima = 0
nx = 0
ny = 0
nnxo = 0
nnyo = 0
nima = bcast_number_to_all(nima)
nx = bcast_number_to_all(nx)
ny = bcast_number_to_all(ny)
nnxo = bcast_number_to_all(nnxo)
nnyo = bcast_number_to_all(nnyo)
if current_window > max(nx, ny):
ERROR("Window size is larger than the original image size", "sx3dvariability", 1)
if current_decimate == 1.:
if current_window !=0:
nx = current_window
ny = current_window
else:
if current_window == 0:
nx = int(nx*current_decimate+0.5)
ny = int(ny*current_decimate+0.5)
else:
nx = int(current_window*current_decimate+0.5)
ny = nx
symbaselen = bcast_number_to_all(symbaselen)
# check FFT prime number
from fundamentals import smallprime
is_fft_friendly = (nx == smallprime(nx))
if not is_fft_friendly:
if myid == main_node:
log_main.add("The target image size is not a product of small prime numbers")
log_main.add("Program adjusts the input settings!")
### two cases
if current_decimate == 1.:
nx = smallprime(nx)
ny = nx
current_window = nx # update
if myid == main_node:
log_main.add("The window size is updated to %d."%current_window)
else:
if current_window == 0:
nx = smallprime(int(nx*current_decimate+0.5))
current_decimate = float(nx)/nnxo
ny = nx
if (myid == main_node):
log_main.add("The decimate rate is updated to %f."%current_decimate)
else:
nx = smallprime(int(current_window*current_decimate+0.5))
ny = nx
current_window = int(nx/current_decimate+0.5)
if (myid == main_node):
log_main.add("The window size is updated to %d."%current_window)
if myid == main_node:
log_main.add("The target image size is %d"%nx)
if radiuspca == -1: radiuspca = nx/2-2
if myid == main_node: log_main.add("%-70s: %d\n"%("Number of projection", nima))
img_begin, img_end = MPI_start_end(nima, number_of_proc, myid)
"""
if options.SND:
from projection import prep_vol, prgs
from statistics import im_diff
from utilities import get_im, model_circle, get_params_proj, set_params_proj
from utilities import get_ctf, generate_ctf
from filter import filt_ctf
imgdata = EMData.read_images(stack, range(img_begin, img_end))
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
bcast_EMData_to_all(vol, myid)
volft, kb = prep_vol(vol)
mask = model_circle(nx/2-2, nx, ny)
varList = []
for i in xrange(img_begin, img_end):
phi, theta, psi, s2x, s2y = get_params_proj(imgdata[i-img_begin])
ref_prj = prgs(volft, kb, [phi, theta, psi, -s2x, -s2y])
if options.CTF:
ctf_params = get_ctf(imgdata[i-img_begin])
ref_prj = filt_ctf(ref_prj, generate_ctf(ctf_params))
diff, A, B = im_diff(ref_prj, imgdata[i-img_begin], mask)
diff2 = diff*diff
set_params_proj(diff2, [phi, theta, psi, s2x, s2y])
varList.append(diff2)
mpi_barrier(MPI_COMM_WORLD)
"""
if options.VAR: # 2D variance images have no shifts
#varList = EMData.read_images(stack, range(img_begin, img_end))
from EMAN2 import Region
for index_of_particle in range(img_begin,img_end):
image = get_im(stack, index_of_proj)
if current_window > 0: varList.append(fdecimate(window2d(image,current_window,current_window), nx,ny))
else: varList.append(fdecimate(image, nx,ny))
else:
from utilities import bcast_number_to_all, bcast_list_to_all, send_EMData, recv_EMData
from utilities import set_params_proj, get_params_proj, params_3D_2D, get_params2D, set_params2D, compose_transform2
from utilities import model_blank, nearest_proj, model_circle, write_text_row, wrap_mpi_gatherv
from applications import pca
from statistics import avgvar, avgvar_ctf, ccc
from filter import filt_tanl
from morphology import threshold, square_root
from projection import project, prep_vol, prgs
from sets import Set
from utilities import wrap_mpi_recv, wrap_mpi_bcast, wrap_mpi_send
import numpy as np
if myid == main_node:
t1 = time()
proj_angles = []
aveList = []
tab = EMUtil.get_all_attributes(stack, 'xform.projection')
for i in range(nima):
t = tab[i].get_params('spider')
phi = t['phi']
theta = t['theta']
psi = t['psi']
x = theta
if x > 90.0: x = 180.0 - x
x = x*10000+psi
proj_angles.append([x, t['phi'], t['theta'], t['psi'], i])
t2 = time()
log_main.add( "%-70s: %d\n"%("Number of neighboring projections", img_per_grp))
log_main.add("...... Finding neighboring projections\n")
log_main.add( "Number of images per group: %d"%img_per_grp)
log_main.add( "Now grouping projections")
proj_angles.sort()
proj_angles_list = np.full((nima, 4), 0.0, dtype=np.float32)
for i in range(nima):
proj_angles_list[i][0] = proj_angles[i][1]
proj_angles_list[i][1] = proj_angles[i][2]
proj_angles_list[i][2] = proj_angles[i][3]
proj_angles_list[i][3] = proj_angles[i][4]
else: proj_angles_list = 0
proj_angles_list = wrap_mpi_bcast(proj_angles_list, main_node, MPI_COMM_WORLD)
proj_angles = []
for i in range(nima):
proj_angles.append([proj_angles_list[i][0], proj_angles_list[i][1], proj_angles_list[i][2], int(proj_angles_list[i][3])])
del proj_angles_list
proj_list, mirror_list = nearest_proj(proj_angles, img_per_grp, range(img_begin, img_end))
all_proj = Set()
for im in proj_list:
for jm in im:
all_proj.add(proj_angles[jm][3])
all_proj = list(all_proj)
index = {}
for i in range(len(all_proj)): index[all_proj[i]] = i
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
log_main.add("%-70s: %.2f\n"%("Finding neighboring projections lasted [s]", time()-t2))
log_main.add("%-70s: %d\n"%("Number of groups processed on the main node", len(proj_list)))
log_main.add("Grouping projections took: %12.1f [m]"%((time()-t2)/60.))
log_main.add("Number of groups on main node: ", len(proj_list))
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
log_main.add("...... Calculating the stack of 2D variances \n")
# Memory estimation. There are two memory consumption peaks
# peak 1. Compute ave, var;
# peak 2. Var volume reconstruction;
# proj_params = [0.0]*(nima*5)
aveList = []
varList = []
#if nvec > 0: eigList = [[] for i in range(nvec)]
dnumber = len(all_proj)# all neighborhood set for assigned to myid
pnumber = len(proj_list)*2. + img_per_grp # aveList and varList
tnumber = dnumber+pnumber
vol_size2 = nx**3*4.*8/1.e9
vol_size1 = 2.*nnxo**3*4.*8/1.e9
proj_size = nnxo*nnyo*len(proj_list)*4.*2./1.e9 # both aveList and varList
orig_data_size = nnxo*nnyo*4.*tnumber/1.e9
reduced_data_size = nx*nx*4.*tnumber/1.e9
full_data = np.full((number_of_proc, 2), -1., dtype=np.float16)
full_data[myid] = orig_data_size, reduced_data_size
if myid != main_node: wrap_mpi_send(full_data, main_node, MPI_COMM_WORLD)
if myid == main_node:
for iproc in range(number_of_proc):
if iproc != main_node:
dummy = wrap_mpi_recv(iproc, MPI_COMM_WORLD)
full_data[np.where(dummy>-1)] = dummy[np.where(dummy>-1)]
del dummy
mpi_barrier(MPI_COMM_WORLD)
full_data = wrap_mpi_bcast(full_data, main_node, MPI_COMM_WORLD)
# find the CPU with heaviest load
minindx = np.argsort(full_data, 0)
heavy_load_myid = minindx[-1][1]
total_mem = sum(full_data)
if myid == main_node:
if current_window == 0:
log_main.add("Nx: current image size = %d. Decimated by %f from %d"%(nx, current_decimate, nnxo))
else:
log_main.add("Nx: current image size = %d. Windowed to %d, and decimated by %f from %d"%(nx, current_window, current_decimate, nnxo))
log_main.add("Nproj: number of particle images.")
log_main.add("Navg: number of 2D average images.")
log_main.add("Nvar: number of 2D variance images.")
log_main.add("Img_per_grp: user defined image per group for averaging = %d"%img_per_grp)
log_main.add("Overhead: total python overhead memory consumption = %f"%overhead_loading)
log_main.add("Total memory) = 4.0*nx^2*(nproj + navg +nvar+ img_per_grp)/1.0e9 + overhead: %12.3f [GB]"%\
(total_mem[1] + overhead_loading))
del full_data
mpi_barrier(MPI_COMM_WORLD)
if myid == heavy_load_myid:
log_main.add("Begin reading and preprocessing images on processor. Wait... ")
ttt = time()
#imgdata = EMData.read_images(stack, all_proj)
imgdata = [ None for im in range(len(all_proj))]
for index_of_proj in range(len(all_proj)):
#image = get_im(stack, all_proj[index_of_proj])
if( current_window > 0): imgdata[index_of_proj] = fdecimate(window2d(get_im(stack, all_proj[index_of_proj]),current_window,current_window), nx, ny)
else: imgdata[index_of_proj] = fdecimate(get_im(stack, all_proj[index_of_proj]), nx, ny)
if (current_decimate> 0.0 and options.CTF):
ctf = imgdata[index_of_proj].get_attr("ctf")
ctf.apix = ctf.apix/current_decimate
imgdata[index_of_proj].set_attr("ctf", ctf)
if myid == heavy_load_myid and index_of_proj%100 == 0:
log_main.add(" ...... %6.2f%% "%(index_of_proj/float(len(all_proj))*100.))
mpi_barrier(MPI_COMM_WORLD)
if myid == heavy_load_myid:
log_main.add("All_proj preprocessing cost %7.2f m"%((time()-ttt)/60.))
log_main.add("Wait untill reading on all CPUs done...")
'''
imgdata2 = EMData.read_images(stack, range(img_begin, img_end))
if options.fl > 0.0:
for k in xrange(len(imgdata2)):
imgdata2[k] = filt_tanl(imgdata2[k], options.fl, options.aa)
if options.CTF:
vol = recons3d_4nn_ctf_MPI(myid, imgdata2, 1.0, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
else:
vol = recons3d_4nn_MPI(myid, imgdata2, symmetry=options.sym, npad=options.npad, xysize=-1, zsize=-1)
if myid == main_node:
vol.write_image("vol_ctf.hdf")
print_msg("Writing to the disk volume reconstructed from averages as : %s\n"%("vol_ctf.hdf"))
del vol, imgdata2
mpi_barrier(MPI_COMM_WORLD)
'''
from applications import prepare_2d_forPCA
from utilities import model_blank
from EMAN2 import Transform
if not options.no_norm:
mask = model_circle(nx/2-2, nx, nx)
if options.CTF:
from utilities import pad
from filter import filt_ctf
from filter import filt_tanl
if myid == heavy_load_myid:
log_main.add("Start computing 2D aveList and varList. Wait...")
ttt = time()
inner=nx//2-4
outer=inner+2
xform_proj_for_2D = [ None for i in range(len(proj_list))]
for i in range(len(proj_list)):
ki = proj_angles[proj_list[i][0]][3]
if ki >= symbaselen: continue
mi = index[ki]
dpar = Util.get_transform_params(imgdata[mi], "xform.projection", "spider")
phiM, thetaM, psiM, s2xM, s2yM = dpar["phi"],dpar["theta"],dpar["psi"],-dpar["tx"]*current_decimate,-dpar["ty"]*current_decimate
grp_imgdata = []
for j in range(img_per_grp):
mj = index[proj_angles[proj_list[i][j]][3]]
cpar = Util.get_transform_params(imgdata[mj], "xform.projection", "spider")
alpha, sx, sy, mirror = params_3D_2D_NEW(cpar["phi"], cpar["theta"],cpar["psi"], -cpar["tx"]*current_decimate, -cpar["ty"]*current_decimate, mirror_list[i][j])
if thetaM <= 90:
if mirror == 0: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, phiM - cpar["phi"], 0.0, 0.0, 1.0)
else: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, 180-(phiM - cpar["phi"]), 0.0, 0.0, 1.0)
else:
if mirror == 0: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(phiM- cpar["phi"]), 0.0, 0.0, 1.0)
else: alpha, sx, sy, scale = compose_transform2(alpha, sx, sy, 1.0, -(180-(phiM - cpar["phi"])), 0.0, 0.0, 1.0)
imgdata[mj].set_attr("xform.align2d", Transform({"type":"2D","alpha":alpha,"tx":sx,"ty":sy,"mirror":mirror,"scale":1.0}))
grp_imgdata.append(imgdata[mj])
if not options.no_norm:
for k in range(img_per_grp):
ave, std, minn, maxx = Util.infomask(grp_imgdata[k], mask, False)
grp_imgdata[k] -= ave
grp_imgdata[k] /= std
if options.fl > 0.0:
for k in range(img_per_grp):
grp_imgdata[k] = filt_tanl(grp_imgdata[k], options.fl, options.aa)
# Because of background issues, only linear option works.
if options.CTF: ave, var = aves_wiener(grp_imgdata, SNR = 1.0e5, interpolation_method = "linear")
else: ave, var = ave_var(grp_imgdata)
# Switch to std dev
# threshold is not really needed,it is just in case due to numerical accuracy something turns out negative.
var = square_root(threshold(var))
set_params_proj(ave, [phiM, thetaM, 0.0, 0.0, 0.0])
set_params_proj(var, [phiM, thetaM, 0.0, 0.0, 0.0])
aveList.append(ave)
varList.append(var)
xform_proj_for_2D[i] = [phiM, thetaM, 0.0, 0.0, 0.0]
'''
if nvec > 0:
eig = pca(input_stacks=grp_imgdata, subavg="", mask_radius=radiuspca, nvec=nvec, incore=True, shuffle=False, genbuf=True)
for k in range(nvec):
set_params_proj(eig[k], [phiM, thetaM, 0.0, 0.0, 0.0])
eigList[k].append(eig[k])
"""
if myid == 0 and i == 0:
for k in xrange(nvec):
eig[k].write_image("eig.hdf", k)
"""
'''
if (myid == heavy_load_myid) and (i%100 == 0):
log_main.add(" ......%6.2f%% "%(i/float(len(proj_list))*100.))
del imgdata, grp_imgdata, cpar, dpar, all_proj, proj_angles, index
if not options.no_norm: del mask
if myid == main_node: del tab
# At this point, all averages and variances are computed
mpi_barrier(MPI_COMM_WORLD)
if (myid == heavy_load_myid):
log_main.add("Computing aveList and varList took %12.1f [m]"%((time()-ttt)/60.))
xform_proj_for_2D = wrap_mpi_gatherv(xform_proj_for_2D, main_node, MPI_COMM_WORLD)
if (myid == main_node):
write_text_row(xform_proj_for_2D, os.path.join(current_output_dir, "params.txt"))
del xform_proj_for_2D
mpi_barrier(MPI_COMM_WORLD)
if options.ave2D:
from fundamentals import fpol
from applications import header
if myid == main_node:
log_main.add("Compute ave2D ... ")
km = 0
for i in range(number_of_proc):
if i == main_node :
for im in range(len(aveList)):
aveList[im].write_image(os.path.join(current_output_dir, options.ave2D), km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
nl = int(nl[0])
for im in range(nl):
ave = recv_EMData(i, im+i+70000)
"""
nm = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
nm = int(nm[0])
members = mpi_recv(nm, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('members', map(int, members))
members = mpi_recv(nm, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('pix_err', map(float, members))
members = mpi_recv(3, MPI_FLOAT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
ave.set_attr('refprojdir', map(float, members))
"""
tmpvol=fpol(ave, nx, nx,1)
tmpvol.write_image(os.path.join(current_output_dir, options.ave2D), km)
km += 1
else:
mpi_send(len(aveList), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
for im in range(len(aveList)):
send_EMData(aveList[im], main_node,im+myid+70000)
"""
members = aveList[im].get_attr('members')
mpi_send(len(members), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
mpi_send(members, len(members), MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
members = aveList[im].get_attr('pix_err')
mpi_send(members, len(members), MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
try:
members = aveList[im].get_attr('refprojdir')
mpi_send(members, 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
except:
mpi_send([-999.0,-999.0,-999.0], 3, MPI_FLOAT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
"""
if myid == main_node:
header(os.path.join(current_output_dir, options.ave2D), params='xform.projection', fimport = os.path.join(current_output_dir, "params.txt"))
mpi_barrier(MPI_COMM_WORLD)
if options.ave3D:
from fundamentals import fpol
t5 = time()
if myid == main_node: log_main.add("Reconstruct ave3D ... ")
ave3D = recons3d_4nn_MPI(myid, aveList, symmetry=options.sym, npad=options.npad)
bcast_EMData_to_all(ave3D, myid)
if myid == main_node:
if current_decimate != 1.0: ave3D = resample(ave3D, 1./current_decimate)
ave3D = fpol(ave3D, nnxo, nnxo, nnxo) # always to the orignal image size
set_pixel_size(ave3D, 1.0)
ave3D.write_image(os.path.join(current_output_dir, options.ave3D))
log_main.add("Ave3D reconstruction took %12.1f [m]"%((time()-t5)/60.0))
log_main.add("%-70s: %s\n"%("The reconstructed ave3D is saved as ", options.ave3D))
mpi_barrier(MPI_COMM_WORLD)
del ave, var, proj_list, stack, alpha, sx, sy, mirror, aveList
'''
if nvec > 0:
for k in range(nvec):
if myid == main_node:log_main.add("Reconstruction eigenvolumes", k)
cont = True
ITER = 0
mask2d = model_circle(radiuspca, nx, nx)
while cont:
#print "On node %d, iteration %d"%(myid, ITER)
eig3D = recons3d_4nn_MPI(myid, eigList[k], symmetry=options.sym, npad=options.npad)
bcast_EMData_to_all(eig3D, myid, main_node)
if options.fl > 0.0:
eig3D = filt_tanl(eig3D, options.fl, options.aa)
if myid == main_node:
eig3D.write_image(os.path.join(options.outpout_dir, "eig3d_%03d.hdf"%(k, ITER)))
Util.mul_img( eig3D, model_circle(radiuspca, nx, nx, nx) )
eig3Df, kb = prep_vol(eig3D)
del eig3D
cont = False
icont = 0
for l in range(len(eigList[k])):
phi, theta, psi, s2x, s2y = get_params_proj(eigList[k][l])
proj = prgs(eig3Df, kb, [phi, theta, psi, s2x, s2y])
cl = ccc(proj, eigList[k][l], mask2d)
if cl < 0.0:
icont += 1
cont = True
eigList[k][l] *= -1.0
u = int(cont)
u = mpi_reduce([u], 1, MPI_INT, MPI_MAX, main_node, MPI_COMM_WORLD)
icont = mpi_reduce([icont], 1, MPI_INT, MPI_SUM, main_node, MPI_COMM_WORLD)
if myid == main_node:
u = int(u[0])
log_main.add(" Eigenvector: ",k," number changed ",int(icont[0]))
else: u = 0
u = bcast_number_to_all(u, main_node)
cont = bool(u)
ITER += 1
del eig3Df, kb
mpi_barrier(MPI_COMM_WORLD)
del eigList, mask2d
'''
if options.ave3D: del ave3D
if options.var2D:
from fundamentals import fpol
from applications import header
if myid == main_node:
log_main.add("Compute var2D...")
km = 0
for i in range(number_of_proc):
if i == main_node :
for im in range(len(varList)):
tmpvol=fpol(varList[im], nx, nx,1)
tmpvol.write_image(os.path.join(current_output_dir, options.var2D), km)
km += 1
else:
nl = mpi_recv(1, MPI_INT, i, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
nl = int(nl[0])
for im in range(nl):
ave = recv_EMData(i, im+i+70000)
tmpvol=fpol(ave, nx, nx,1)
tmpvol.write_image(os.path.join(current_output_dir, options.var2D), km)
km += 1
else:
mpi_send(len(varList), 1, MPI_INT, main_node, SPARX_MPI_TAG_UNIVERSAL, MPI_COMM_WORLD)
for im in range(len(varList)):
send_EMData(varList[im], main_node, im+myid+70000)# What with the attributes??
mpi_barrier(MPI_COMM_WORLD)
if myid == main_node:
from applications import header
header(os.path.join(current_output_dir, options.var2D), params = 'xform.projection',fimport = os.path.join(current_output_dir, "params.txt"))
mpi_barrier(MPI_COMM_WORLD)
if options.var3D:
if myid == main_node: log_main.add("Reconstruct var3D ...")
t6 = time()
# radiusvar = options.radius
# if( radiusvar < 0 ): radiusvar = nx//2 -3
res = recons3d_4nn_MPI(myid, varList, symmetry = options.sym, npad=options.npad)
#res = recons3d_em_MPI(varList, vol_stack, options.iter, radiusvar, options.abs, True, options.sym, options.squ)
if myid == main_node:
from fundamentals import fpol
if current_decimate != 1.0: res = resample(res, 1./current_decimate)
res = fpol(res, nnxo, nnxo, nnxo)
set_pixel_size(res, 1.0)
res.write_image(os.path.join(current_output_dir, options.var3D))
log_main.add("%-70s: %s\n"%("The reconstructed var3D is saved as ", options.var3D))
log_main.add("Var3D reconstruction took %f12.1 [m]"%((time()-t6)/60.0))
log_main.add("Total computation time %f12.1 [m]"%((time()-t0)/60.0))
log_main.add("sx3dvariability finishes")
from mpi import mpi_finalize
mpi_finalize()
if RUNNING_UNDER_MPI: global_def.MPI = False
global_def.BATCH = False
def cml_open_proj(stack, ir, ou, lf, hf, dpsi=1):
from projection import cml_sinogram
from utilities import model_circle, get_params_proj, model_blank, get_im
from fundamentals import fftip
from filter import filt_tanh
# number of projections
if type(stack) == type(""): nprj = EMUtil.get_image_count(stack)
else: nprj = len(stack)
Prj = [] # list of projections
Ori = [
-1
] * 4 * nprj # orientation intial (phi, theta, psi, index) for each projection
for i in xrange(nprj):
image = get_im(stack, i)
# read initial angles if given
try:
Ori[4 * i], Ori[4 * i +
1], Ori[4 * i +
2], s2x, s2y = get_params_proj(image)
except:
pass
if (i == 0):
nx = image.get_xsize()
if (ou < 1): ou = nx // 2 - 1
diameter = int(2 * ou)
mask2D = model_circle(ou, nx, nx)
if ir > 0: mask2D -= model_circle(ir, nx, nx)
# normalize under the mask
[mean_a, sigma, imin, imax] = Util.infomask(image, mask2D, True)
image -= mean_a
Util.mul_scalar(image, 1.0 / sigma)
Util.mul_img(image, mask2D)
# sinogram
sino = cml_sinogram(image, diameter, dpsi)
# prepare the cut positions in order to filter (lf: low freq; hf: high freq)
ihf = min(int(2 * hf * diameter), diameter + (diameter + 1) % 2)
ihf = ihf + (ihf + 1) % 2 # index ihf must be odd to take the img part
ilf = max(int(2 * lf * diameter), 0)
ilf = ilf + ilf % 2 # index ilf must be even to fall in the real part
bdf = ihf - ilf + 1
# process lines
nxe = sino.get_xsize()
nye = sino.get_ysize()
prj = model_blank(bdf, 2 * nye)
pp = model_blank(nxe, 2 * nye)
for li in xrange(nye):
# get the line li
line = Util.window(sino, nxe, 1, 1, 0, li - nye // 2, 0)
# u2 (not improve the results)
#line = filt_tanh(line, ou / float(nx), ou / float(nx))
# normalize this line
[mean_l, sigma_l, imin, imax] = Util.infomask(line, None, True)
line = (line - mean_l) / sigma_l
# fft
fftip(line)
# filter (cut part of coef) and create mirror line
Util.cml_prepare_line(prj, line, ilf, ihf, li, nye)
# store the projection
Prj.append(prj)
return Prj, Ori
def main():
from utilities import get_input_from_string
progname = os.path.basename(sys.argv[0])
usage = (
progname
+ " stack output_average --radius=particle_radius --xr=xr --yr=yr --ts=ts --thld_err=thld_err --num_ali=num_ali --fl=fl --aa=aa --CTF --verbose --stables"
)
parser = OptionParser(usage, version=SPARXVERSION)
parser.add_option("--radius", type="int", default=-1, help=" particle radius for alignment")
parser.add_option(
"--xr",
type="string",
default="2 1",
help="range for translation search in x direction, search is +/xr (default 2,1)",
)
parser.add_option(
"--yr",
type="string",
default="-1",
help="range for translation search in y direction, search is +/yr (default = same as xr)",
)
parser.add_option(
"--ts",
type="string",
default="1 0.5",
help="step size of the translation search in both directions, search is -xr, -xr+ts, 0, xr-ts, xr, can be fractional (default: 1,0.5)",
)
parser.add_option("--thld_err", type="float", default=0.75, help="threshld of pixel error (default = 0.75)")
parser.add_option(
"--num_ali", type="int", default=5, help="number of alignments performed for stability (default = 5)"
)
parser.add_option("--maxit", type="int", default=30, help="number of iterations for each xr (default = 30)")
parser.add_option(
"--fl",
type="float",
default=0.3,
help="cut-off frequency of hyperbolic tangent low-pass Fourier filter (default = 0.3)",
)
parser.add_option(
"--aa", type="float", default=0.2, help="fall-off of hyperbolic tangent low-pass Fourier filter (default = 0.2)"
)
parser.add_option("--CTF", action="store_true", default=False, help="Use CTF correction during the alignment ")
parser.add_option(
"--verbose", action="store_true", default=False, help="print individual pixel error (default = False)"
)
parser.add_option(
"--stables",
action="store_true",
default=False,
help="output the stable particles number in file (default = False)",
)
parser.add_option(
"--method", type="string", default=" ", help="SHC (standard method is default when flag is ommitted)"
)
(options, args) = parser.parse_args()
if len(args) != 1 and len(args) != 2:
print "usage: " + usage
print "Please run '" + progname + " -h' for detailed options"
else:
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
from applications import within_group_refinement, ali2d_ras
from pixel_error import multi_align_stability
from utilities import write_text_file, write_text_row
global_def.BATCH = True
xrng = get_input_from_string(options.xr)
if options.yr == "-1":
yrng = xrng
else:
yrng = get_input_from_string(options.yr)
step = get_input_from_string(options.ts)
class_data = EMData.read_images(args[0])
nx = class_data[0].get_xsize()
ou = options.radius
num_ali = options.num_ali
if ou == -1:
ou = nx / 2 - 2
from utilities import model_circle, get_params2D, set_params2D
mask = model_circle(ou, nx, nx)
if options.CTF:
from filter import filt_ctf
for im in xrange(len(class_data)):
# Flip phases
class_data[im] = filt_ctf(class_data[im], class_data[im].get_attr("ctf"), binary=1)
for im in class_data:
im.set_attr("previousmax", -1.0e10)
try:
t = im.get_attr("xform.align2d") # if they are there, no need to set them!
except:
try:
t = im.get_attr("xform.projection")
d = t.get_params("spider")
set_params2D(im, [0.0, -d["tx"], -d["ty"], 0, 1.0])
except:
set_params2D(im, [0.0, 0.0, 0.0, 0, 1.0])
all_ali_params = []
for ii in xrange(num_ali):
ali_params = []
if options.verbose:
ALPHA = []
SX = []
SY = []
MIRROR = []
if xrng[0] == 0.0 and yrng[0] == 0.0:
avet = ali2d_ras(
class_data,
randomize=True,
ir=1,
ou=ou,
rs=1,
step=1.0,
dst=90.0,
maxit=options.maxit,
check_mirror=True,
FH=options.fl,
FF=options.aa,
)
else:
avet = within_group_refinement(
class_data,
mask,
True,
1,
ou,
1,
xrng,
yrng,
step,
90.0,
maxit=options.maxit,
FH=options.fl,
FF=options.aa,
method=options.method,
)
from utilities import info
# print " avet ",info(avet)
for im in class_data:
alpha, sx, sy, mirror, scale = get_params2D(im)
ali_params.extend([alpha, sx, sy, mirror])
if options.verbose:
ALPHA.append(alpha)
SX.append(sx)
SY.append(sy)
MIRROR.append(mirror)
all_ali_params.append(ali_params)
if options.verbose:
write_text_file([ALPHA, SX, SY, MIRROR], "ali_params_run_%d" % ii)
"""
avet = class_data[0]
from utilities import read_text_file
all_ali_params = []
for ii in xrange(5):
temp = read_text_file( "ali_params_run_%d"%ii,-1)
uuu = []
for k in xrange(len(temp[0])):
uuu.extend([temp[0][k],temp[1][k],temp[2][k],temp[3][k]])
all_ali_params.append(uuu)
"""
stable_set, mir_stab_rate, pix_err = multi_align_stability(
all_ali_params, 0.0, 10000.0, options.thld_err, options.verbose, 2 * ou + 1
)
print "%4s %20s %20s %20s %30s %6.2f" % (
"",
"Size of set",
"Size of stable set",
"Mirror stab rate",
"Pixel error prior to pruning the set above threshold of",
options.thld_err,
)
print "Average stat: %10d %20d %20.2f %15.2f" % (len(class_data), len(stable_set), mir_stab_rate, pix_err)
if len(stable_set) > 0:
if options.stables:
stab_mem = [[0, 0.0, 0] for j in xrange(len(stable_set))]
for j in xrange(len(stable_set)):
stab_mem[j] = [int(stable_set[j][1]), stable_set[j][0], j]
write_text_row(stab_mem, "stable_particles.txt")
stable_set_id = []
particle_pixerr = []
for s in stable_set:
stable_set_id.append(s[1])
particle_pixerr.append(s[0])
from fundamentals import rot_shift2D
avet.to_zero()
l = -1
print "average parameters: angle, x-shift, y-shift, mirror"
for j in stable_set_id:
l += 1
print " %4d %4d %12.2f %12.2f %12.2f %1d" % (
l,
j,
stable_set[l][2][0],
stable_set[l][2][1],
stable_set[l][2][2],
int(stable_set[l][2][3]),
)
avet += rot_shift2D(
class_data[j], stable_set[l][2][0], stable_set[l][2][1], stable_set[l][2][2], stable_set[l][2][3]
)
avet /= l + 1
avet.set_attr("members", stable_set_id)
avet.set_attr("pix_err", pix_err)
avet.set_attr("pixerr", particle_pixerr)
avet.write_image(args[1])
global_def.BATCH = False
